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60Tue, 26 Mar 2019 11:01:01 06001711.02827journals/corr/1711.028272Inverse Reward DesigncapybaraletThe method they use basically tells the robot to reason as follows:
1. The human gave me a reward function $\tilde{r}$, selected in order to get me to behave the way they wanted.
2. So I should favor reward functions which produce that kind of behavior.
This amounts to doing RL (step 1) followed by IRL on the learned policy (step 2); see the final paragraph of section 4.
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.02827#capybaralet
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.02827#capybaraletTue, 19 Mar 2019 23:02:23 06001903.00374journals/corr/1903.003743ModelBased Reinforcement Learning for AtariAnkesh AnandThis paper shows exciting results on using Modelbased RL for Atari.
Modelbased RL has shown impressive improvements in sample efficiency on Mujoco tasks ([Chua et. al, 2018]()), so its nice to see that the sample efficiency improvements carry over to Pixelbased envs like Atari too.
Specifically, the authors show that their modelbased method can do well on several Atari games after training on only 100K env steps (400K frames with FrameSkip 4) which roughly corresponds to 2 hours of game ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1903.00374#ankeshanand
http://www.shortscience.org/paper?bibtexKey=journals/corr/1903.00374#ankeshanandTue, 05 Mar 2019 17:25:09 07001902.08605journals/corr/1902.086052Centroid Networks for FewShot Clustering and Unsupervised FewShot ClassificationgabrielDisclaimer: I am the first author.
# Executive summary
 The authors propose a new method, [*Centroid Networks*](), for learning to cluster.
 Given example clusterings of data, the goal is to learn how to cluster new data following the same criterion.
 Centroid Networks basically consist of running Kmeans on Prototypical Network features, plus many tricks.
 They evaluate Centroid Networks on Omniglot and miniImageNet (supervised fewshot classification benchmarks).
 Centroid Networks can...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1902.08605#gabriel
http://www.shortscience.org/paper?bibtexKey=journals/corr/1902.08605#gabrielWed, 27 Feb 2019 21:12:14 07001706.03922journals/corr/WangJC172Analyzing the Robustness of Nearest Neighbors to Adversarial ExamplesDavid StutzWang et al. discuss the robustness of $k$nearest neighbors against adversarial perturbations, providing both a theoretical analysis as well as a robust 1nearest neighbor version. Specifically, for low $k$ it is shown that nearest neighbor is usually not robust. Here, robustness is judged in a distributional sense; so for fixed and low $k$, the lowest distance of any training sample to an adversarial sample tends to zero, even if the training set size increases. For $k \in \mathcal{O}(dn \log n...
http://www.shortscience.org/paper?bibtexKey=journals/corr/WangJC17#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/WangJC17#davidstutzSat, 16 Feb 2019 18:27:13 0700conf/cvpr/SharifBR182On the Suitability of LpNorms for Creating and Preventing Adversarial ExamplesDavid StutzSharif et al. study the effectiveness of $L_p$ norms for creating adversarial perturbations. In this context, their main discussion revolves around whether $L_p$ norms are sufficient and/or necessary for perceptual similarity. Their main conclusion is that $L_p$ norms are neither necessary nor sufficient to ensure perceptual similarity. For example, an adversarial example might be within a specific $L_p$ bal, but humans might still identify it as not similar enough to the originally attacked sam...
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/SharifBR18#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/SharifBR18#davidstutzSat, 16 Feb 2019 18:17:41 0700conf/nips/RatnerEHDR173Learning to Compose DomainSpecific Transformations for Data Augmentation.David StutzRatner et al. Train an adversarial generative network to learn domainspecific sequences of transformations useful for data augmentation. In particular, as indicated in Figure 1, the generator learns to predict sequences of userspecified transformations and the classifier is intended to distinguish the original images from the transformed ones. For training, the authors use reinforcement learning, because the transformations are not necessarily differentiable – which makes usage of the propos...
http://www.shortscience.org/paper?bibtexKey=conf/nips/RatnerEHDR17#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/nips/RatnerEHDR17#davidstutzSat, 16 Feb 2019 18:08:46 07001803.04765journals/corr/1803.047653Deep kNearest Neighbors: Towards Confident, Interpretable and Robust Deep LearningDavid StutzPapernot and McDaniel introduce deep knearest neighbors where nearest neighbors are found at each intermediate layer in order to improve interpretbaility and robustness. Personally, I really appreciated reading this paper; thus, I will not only discuss the actually proposed method but also highlight some ideas from their thorough survey and experimental results.
First, Papernot and McDaniel provide a quite thorough survey of relevant work in three disciplines: confidence, interpretability and ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1803.04765#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/1803.04765#davidstutzSat, 16 Feb 2019 18:05:33 07001801.04693journals/corr/1801.046933Towards Imperceptible and Robust Adversarial Example Attacks against Neural NetworksDavid StutzLuo et al. Propose a method to compute lessperceptible adversarial examples compared to standard methods constrained in $L_p$ norms. In particular, they consider the local variation of the image and argue that humans are more likely to notice larger variations in lowvariance regions than viceversa. The sensitivity of a pixel is therefore defined as one over its local variance, meaning that it is more sensitive to perturbations. They propose a simple algorithm which iteratively sorts pixels by...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1801.04693#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/1801.04693#davidstutzSat, 16 Feb 2019 17:36:45 07001706.04599journals/corr/1706.045993On Calibration of Modern Neural NetworksDavid StutzGuo et al. study calibration of deep neural networks as postprocessing step. Here, calibration means a correction of the predicted confidence scores as these are commonlz too overconfident in recent deep networks. They consider several stateoftheart postprocessing steps for calibration, but surprisingly, they show that a simple linear mapping, or even scaling, works surprisingly well. So if $z_i$ are the logits of the network, then (the network being fixed) a parameter $T$ is found such tha...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.04599#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.04599#davidstutzSat, 16 Feb 2019 17:30:04 07001710.10547journals/corr/abs1710105472Interpretation of Neural Networks is FragileDavid StutzGhorbani et al. Show that neural network visualization techniques, often introduced to improve interpretability, are susceptible to adversarial examples. For example, they consider common featureimportance visualization techniques and aim to find an advesarial example that does not change the predicted label but the original interpretation – e.g., as measured on some of the most important features. Examples of the socalled top1000 attack where the 1000 most important features are changed du...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171010547#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171010547#davidstutzSat, 16 Feb 2019 17:15:35 07001801.02612journals/corr/abs1801026122Spatially Transformed Adversarial ExamplesDavid StutzXiao et al. propose adversarial examples based on spatial transformations. Actually, this work is very similar to the adversarial deformations of [1]. In particular, a deformation flow field is optimized (allowing individual deformations per pixel) to cause a misclassification. The distance of the perturbation is computed on the flow field directly. Examples on MNIST are shown in Figure 1 – it can clearly be seen that most pixels are moved individually and no kind of smoothness is enforced. Th...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180102612#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180102612#davidstutzMon, 11 Feb 2019 18:37:03 07001710.10733journals/corr/1710.107332Attacking the Madry Defense Model with $L_1$based Adversarial ExamplesDavid StutzSharma and Chen provide an experimental comparison of different stateoftheart attacks against the adversarial training defense by Madry et al. [1]. They consider several attacks, including the Carlini Wagner attacks [2], elastic net attacks [3] as well as projected gradient descent [1]. Their experimental finding – that the defense by Madry et al. Can be broken by increasing the allowed perturbation size (i.e., epsilon) – should not be surprising. Every network trained adversarially will ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1710.10733#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/1710.10733#davidstutzMon, 11 Feb 2019 18:26:32 07001802.06627journals/corr/abs1802066272Robustness of RotationEquivariant Networks to Adversarial PerturbationsDavid StutzDumont et al. Compare different adversarial transformation attacks (including rotations and translations) against common as well as rotationinvariant convolutional neural networks. On MNIST, CIFAR10 and ImageNet, they consider translations, rotations as well as the attack of [1] based on spatial transformer networks. Additionally, they consider rotationinvariant convolutional neural networks – however, both the attacks and the networks are not discussed/introduced in detail. The results ar...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180206627#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180206627#davidstutzMon, 11 Feb 2019 18:11:30 07001711.09115journals/corr/abs1711091152Geometric robustness of deep networks: analysis and improvementDavid StutzKanbak et al. propose ManiFool, a method to determine a network’s invariance to transformations by iteratively finding adversarial transformations. In particular, given a class of transformations to consider, ManiFool iteratively alternates two steps. First, a gradient step is taken in order to move into an adversarial direction; then, the obtained perturbation/direction is projected back to the space of allowed transformations. While the details are slightly more involved, I found that this a...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171109115#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171109115#davidstutzMon, 11 Feb 2019 18:06:41 07001712.09665journals/corr/abs1712096652Adversarial PatchDavid StutzBrown et al. Introduce a universal adversarial patch that, when added to an image, will cause a targeted misclassification. The concept is illustrated in Figure 1; essentially, a “sticker” is computed that, when placed randomly on an image, causes misclassification. In practice, the objective function optimized can be written as
$\max_p \mathbb{E}_{x\sim X, t \sim T, l \sim L} \log p(yA(p,x,l,t))$
where $y$ is the target label and $X$, $T$ and $L$ are te data space, the transformation spa...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171209665#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171209665#davidstutzMon, 11 Feb 2019 18:03:29 07001802.00420journals/corr/abs1802004203Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial ExamplesDavid StutzAthalye et al. propose methods to circumvent different types of defenses against adversarial example based on obfuscated gradients. In particular, they identify three types of obfuscated gradients: shattered gradients (e.g., caused by undifferentiable parts of a network or through numerical instability), stochastic gradients, and exploding and vanishing gradients. These phenomena all influence the effectiveness of gradientbased attacks. Athalye et al. Give several indicators of how to find out ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180200420#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180200420#davidstutzMon, 11 Feb 2019 17:56:52 07001804.07729journals/corr/abs1804077292ADef: an Iterative Algorithm to Construct Adversarial DeformationsDavid StutzAlaifari et al. propose an iterative attack to construct adversarial deformations of images. In particular, and in contrast to general adversarial perturbations, adversarial deformations are described through a deformation vector field – and the corresponding norm of this vector field may be bounded; an illustration can be found in Figure 1. The adversarial deformation is computed iteratively where the deformation itself is expressed in a differentiable manner. In contrast to very simple trans...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180407729#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180407729#davidstutzMon, 11 Feb 2019 17:51:52 07001806.11146journals/corr/abs1806111462Adversarial Reprogramming of Neural NetworksDavid StutzElsayed et al. use universal adversarial examples to reprogram neural networks in order to perform different tasks. In particular, e.g., on ImageNet, an adversarial example
$\delta = \tanh(W \cdot M)$
is computed where $M$ is a mask image (see Figure 1, in the paper the mask image essentially embeds a smaller image into an ImageNetsized image) and $W$ is the adversarial perturbation itself (note that the notaiton was changed slightly for simplification). The hyperbolic tangent constraints the...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180611146#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180611146#davidstutzSun, 10 Feb 2019 18:57:07 07001804.03286journals/corr/abs1804032862On the Robustness of the CVPR 2018 WhiteBox Adversarial Example DefensesDavid StutzAthalye and Carlini present experiments showing that pixel deflection [1] and highlevel guided denoiser [2] are ineffective as defense against adversarial examples. In particular, they show that these defenses are not effective against the (currently) strongest firstorder attack, projected gradient descent. Here, they also comment on the right threat model to use and explicitly state that the attacker would know the employed defense – which intuitively makes much sense when evaluating defens...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180403286#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180403286#davidstutzSun, 10 Feb 2019 18:44:29 0700conf/nips/TeoGRS073Convex Learning with InvariancesDavid StutzTeo et al. propose a convex, robust learning framework allowing to integrate invariances into SVM training. In particular, they consider a set of valid transformations and define the cost of a training sample (i.e., pair of data and label) as the loss under the worst case transformation – this definition is very similar to robust optimization or adversarial training. Then, a convex upper bound on this cost is derived. Given, that the worst case transformation can be found efficiently, two diff...
http://www.shortscience.org/paper?bibtexKey=conf/nips/TeoGRS07#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/nips/TeoGRS07#davidstutzSun, 10 Feb 2019 18:36:31 0700conf/cvpr/DongLPS0HL182Boosting Adversarial Attacks With MomentumDavid StutzDong et al. introduce momentum into iterative whitebox adversarial examples and also show that attacking ensembles of models improves transferability. Specifically, their contribution is twofold. First, some iterative whitebox attacks are extended to include a momentum term. As in optimization or learning, the main motivation is to avoid local maxima and have faster convergence. In experiments, they show that momentum is able to increase the success rates of attacks.
Second, to improve the tr...
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/DongLPS0HL18#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/DongLPS0HL18#davidstutzSun, 10 Feb 2019 18:03:00 07001711.00851journals/corr/abs1711008512Provable defenses against adversarial examples via the convex outer adversarial polytopeDavid StutzWong and Kolter propose a method for learning provablyrobust, deep, ReLU based networks by considering the socalled adversarial polytope of finallayer activations reachable through adversarial examples. Overall, the proposed approach has some similarities to adversarial training in that the overall objective can be written as
$\min_\theta \sum_{i = 1}^N \max_{\\Delta\_\infty \leq \epsilon} L(f_\theta(x_i + \Delta), y_i)$.
However, in contrast to previous work, the inner maximization prob...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171100851#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171100851#davidstutzSun, 10 Feb 2019 17:56:35 07001805.12152journals/corr/abs1805121522There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)David StutzTsipras et al. investigate the tradeoff between classification accuracy and adversarial robustness. In particular, on a very simple toy dataset, they proof that such a tradeoff exists; this means that very accurate models will also have low robustness. Overall, on this dataset, they find that there exists a sweetspot where the accuracy is 70% and the adversarial accuracy (i.e., accuracy on adversarial examples) is 70%. Using adversarial training to obtain robust networks, they additionally sh...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180512152#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180512152#davidstutzSun, 10 Feb 2019 17:45:22 0700conf/nips/SchmidtSTTM184Adversarially Robust Generalization Requires More DataDavid StutzSchmidt et al. theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Theoretically, they analyze two very simple families of datasets, e.g., consisting of two Gaussian distributions corresponding to a twoclass problem. On such datasets, they proof that “robust generalization”, i.e., generalization to adversarial examples, required much higher sample complexity compared to regular generlization,...
http://www.shortscience.org/paper?bibtexKey=conf/nips/SchmidtSTTM18#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/nips/SchmidtSTTM18#davidstutzSun, 10 Feb 2019 17:37:53 07001803.00940journals/corr/abs1803009402Protecting JPEG Images Against Adversarial AttacksDavid StutzMotivated by JPEG compression, Prakash et al. propose an adaptive quantization scheme as defense against adversarial attacks. They argue that JPEG experimentally reduces adversarial noise; however, it is difficult to automatically decide on the level of compression as it also influences a classifier’s performance. Therefore, Prakash et al. use a saliency detector to identify background region, and then apply adaptive quantization – with coarser detail at the background – to reduce the impa...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180300940#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180300940#davidstutzSun, 10 Feb 2019 17:30:41 07001803.06373journals/corr/abs1803063733Adversarial Logit PairingDavid StutzKannan et al. propose a defense against adversarial examples called adversarial logit pairing where the logits of clean and adversarial example are regularized to be similar. In particular, during adversarial training, they add a regularizer of the form
$\lambda L(f(x), f(x’))$
were $L$ is, for example, the $L_2$ norm and $f(x’)$ the logits corresponding to adversarial example $x’$ (corresponding to clean example $x$). Intuitively, this is a very simple approach – adversarial training ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180306373#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180306373#davidstutzSun, 10 Feb 2019 17:25:06 07001802.07124journals/corr/abs1802071244Outdistribution training confers robustness to deep neural networksDavid Stutz...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180207124#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180207124#davidstutzSun, 10 Feb 2019 17:20:43 0700conf/cvpr/AkhtarLM182Defense Against Universal Adversarial PerturbationsDavid StutzAkhtar et al. Propose a rectification and detection scheme as defense against universal adversarial perturbations. Their overall approach is illustrated in Figure 1 an briefly summarized as follows. Given a classifier with fixed weights, a rectification network (the socalled perturbation rectifying network – PRN) is trained in order to “undo” the perturbations. This network can be trained on a set of clean and perturbed images using the classifier’s loss. Second, based on the discrete c...
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/AkhtarLM18#davidstutz
http://www.shortscience.org/paper?bibtexKey=conf/cvpr/AkhtarLM18#davidstutzSun, 10 Feb 2019 16:56:11 07001803.07994journals/corr/abs1803079942Adversarial Defense based on StructuretoSignal AutoencodersDavid StutzFolz et al. propose an autoencoder based defense against adversarial examples. In particular, they propose structuretosignal autoencoders, S2SNets, as defense mechanism – this autoencoder is first trained in an unsupervised fashion to reconstruct images (which can be done independent of attack models or the classification network under attack). Then, the network’s decoder is fine tuned using gradients from the classification network. Their main argumentation is that the gradients of the...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180307994#davidstutz
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180307994#davidstutzSun, 10 Feb 2019 16:50:03 07001812.09916journals/corr/1812.099163Improving MMDGAN Training with Repulsive Loss Functionrichard_wth**TL;DR**: Rearranging the terms in Maximum Mean Discrepancy yields a much better loss function for the discriminator of Generative Adversarial Nets.
**Keywords**: Generative adversarial nets, Maximum Mean Discrepancy, spectral normalization, convolutional neural networks, Gaussian kernel, local stability.
**Summary**
Generative adversarial nets (GANs) are widely used to learn the data sampling process and are notoriously difficult to train. The training of GANs may be improved from three asp...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1812.09916#richardwth
http://www.shortscience.org/paper?bibtexKey=journals/corr/1812.09916#richardwthTue, 15 Jan 2019 05:07:15 07001802.03685journals/corr/abs1802036852Learning a SAT Solver from SingleBit SupervisionameroyerThe goal is to solve SAT problems with weak supervision: In that case a model is trained only to predict ***the satisfiability*** of a formula in conjunctive normal form. As a byproduct, when the formula is satisfiable, an actual satisfying assignment can be worked out by clustering the network's activations in most cases.
* **Pros (+):** Weak supervision, interesting structured architecture, seems to generalize nicely to harder problems by increasing the number message passing iterations.
...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180203685#ameroyer
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs180203685#ameroyerMon, 14 Jan 2019 12:58:19 07001809.01442journals/corr/1809.014423Data Augmentation for Skin Lesion AnalysisFábio Perez_Disclaimer: I'm the first author of this paper._
The code for this paper can be found at .
In this work, we wanted to compare different data augmentation scenarios for skin lesion analysis. We tried 13 scenarios, including commonly used augmentation techniques (color and geometry transformations), unusual ones (random erasing, elastic transformation, and a novel lesion mix to simulate collision lesions), and a combination of those.
Examples of the augmentation scenarios:
a) no augmentati...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.01442#fabioperez
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.01442#fabioperezMon, 14 Jan 2019 10:23:45 07001802.10217journals/corr/1802.102174Investigating Human Priors for Playing Video GamesFábio PerezAuthors investigated why humans play some video games better than machines. That is the case for games that do not have continuous rewards (e.g., scores). They experimented with a game  inspired by _Montezuma's Revenge_  in which the player has to climb stairs, collect keys and jump over enemies. RL algorithms can only know if they succeed if they finish the game, as there is no rewards during the gameplay, so they tend to do much worse than humans in these games.
To compare between humans ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.10217#fabioperez
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.10217#fabioperezFri, 28 Dec 2018 20:19:27 07001710.10196journals/corr/abs1710101963Progressive Growing of GANs for Improved Quality, Stability, and VariationANIRUDH NJ
## **Keywords**
Progressive GAN , High resolution generator

## **Summary**
1. **Introduction**
1. **Goal of the paper**
1. Generation of very high quality images using progressively increasing size of the generator and discriminator.
1. Improved training and stability of GANs.
1. New metric for evaluating GAN results.
1. A high quality version of CELEBAHQ dataset.
1. **Previous Research**
1. Generative methods help to produce new s...
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171010196#anirudhnj
http://www.shortscience.org/paper?bibtexKey=journals/corr/abs171010196#anirudhnjFri, 28 Dec 2018 18:33:17 07001810.09136journals/corr/1810.091364Do Deep Generative Models Know What They Don't Know?ameroyerCNNs predictions are known to be very sensitive to adversarial examples, which are samples generated to be wrongly classifiied with high confidence. On the other hand, probabilistic generative models such as `PixelCNN` and `VAEs` learn a distribution over the input domain hence could be used to detect ***outofdistribution inputs***, e.g., by estimating their likelihood under the data distribution. This paper provides interesting results showing that distributions learned by generative models a...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.09136#ameroyer
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.09136#ameroyerMon, 17 Dec 2018 10:20:46 07001806.07366journals/corr/1806.073662Neural Ordinary Differential EquationswassnameSummary by senior author [duvenaud on hackernews]().
A few years ago, everyone switched their deep nets to "residual nets". Instead of building deep models like this:
h1 = f1(x)
h2 = f2(h1)
h3 = f3(h2)
h4 = f3(h3)
y = f5(h4)
They now build them like this:
h1 = f1(x) + x
h2 = f2(h1) + h1
h3 = f3(h2) + h2
h4 = f4(h3) + h3
y = f5(h4) + h4
Where f1, f2, etc are neural net layers. The idea is that it's easier to model a small change to an almostcorrec...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.07366#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.07366#wassnameSun, 16 Dec 2018 04:33:03 07001802.04865journals/corr/1802.048652Learning Confidence for OutofDistribution Detection in Neural Networkselbaro
## Summary
In a prior work 'On Calibration of Modern Nueral Networks', temperature scailing is used for outputing confidence. This is done at inference stage, and does not change the existing classifier. This paper considers the confidence at training stage, and directly outputs the confidence from the network.
## Architecture
An additional branch for confidence is added after the penultimate layer, in parallel to logits and probs (Figure 2).
## Training
The network outputs the prob $p$ and...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.04865#elbaro
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.04865#elbaroMon, 10 Dec 2018 07:30:06 07001706.02690journals/corr/1706.026902Enhancing The Reliability of Outofdistribution Image Detection in Neural Networkselbaro## Task
Add '**rejection**' output to an existing classification model with softmax layer.
## Method
1. Choose some threshold $\delta$ and temperature $T$
2. Add a perturbation to the input x (eq 2),
let $\tilde x = x  \epsilon \text{sign}(\nabla_x \log S_{\hat y}(x;T))$
3. If $p(\tilde x;T)\le \delta$, rejects
4. If not, return the output of the original classifier
$p(\tilde x;T)$ is the max prob with temperature scailing for input $\tilde x$
$\delta$ and $T$ are manually chosen.
...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.02690#elbaro
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.02690#elbaroMon, 10 Dec 2018 07:17:15 07001706.04599journals/corr/1706.045992On Calibration of Modern Neural Networkselbaro## Task
A neural network for classification typically has a **softmax** layer and outputs the class with the max probability. However, this probability does not represent the **confidence**. If the average confidence (average of max probs) for a dataset matches the accuracy, it is called **wellcalibrated**. Old models like LeNet (1998) was wellcalibrated, but modern networks like ResNet (2016) are no longer wellcalibrated. This paper explains what caused this and compares various calibration...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.04599#elbaro
http://www.shortscience.org/paper?bibtexKey=journals/corr/1706.04599#elbaroMon, 10 Dec 2018 05:52:45 07001811.04551journals/corr/1811.045513Learning Latent Dynamics for Planning from Pixelswassname**Summary**: This paper presents three tricks that make modelbased reinforcement more reliable when tested in tasks that require walking and balancing. The tricks are 1) are planning based on features, 2) using a recursive network that mixes probabilistic and deterministic information, and 3) looking forward multiple steps.
**Longer summary**
Imagine playing pool, armed with a tablet that can predict exactly where the ball will bounce, and the next bounce, and so on. That would be a huge adva...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.04551#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.04551#wassnameSun, 09 Dec 2018 11:50:05 07001807.03146journals/corr/1807.031462Discovery of Latent 3D Keypoints via Endtoend Geometric ReasoningKrishna MurthyWhat the paper is about:
KeypointNet learns the optimal set of 3D keypoints and their 2D detectors for a specified downstream task. The authors demonstrate this by extracting 3D keypoints and their 2D detectors for the task of relative pose estimation across views. They show that, using keypoints extracted by KeypointNet, relative pose estimates are superior to ones that are obtained from a supervised set of keypoints.
Approach:
Training samples for KeypointNet comprise two views (images) of a...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.03146#krishnamurthy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.03146#krishnamurthyThu, 06 Dec 2018 08:04:18 0700conf/nips/GomezRUG173The Reversible Residual Network: Backpropagation Without Storing Activations.ameroyerResidual Networks (ResNets) have greatly advanced the stateoftheart in Deep Learning by making it possible to train much deeper networks via the addition of skip connections. However, in order to compute gradients during the backpropagation pass, all the units' activations have to be stored during the feedforward pass, leading to high memory requirements for these very deep networks.
Instead, the authors propose a **reversible architecture** based on ResNets, in which activations at one l...
http://www.shortscience.org/paper?bibtexKey=conf/nips/GomezRUG17#ameroyer
http://www.shortscience.org/paper?bibtexKey=conf/nips/GomezRUG17#ameroyerWed, 05 Dec 2018 15:14:10 07001712.09913journals/corr/1712.099133Visualizing the Loss Landscape of Neural Netsdaisukelab Presents a simple visualization method based on “filter normalization.”
 Observed that __the deeper networks become, neural loss landscapes become more chaotic__; causes a dramatic drop in generalization error, and ultimately to a lack of trainability.
 Observed that __skip connections promote flat minimizers and prevent the transition to chaotic behavior__; helps explain why skip connections are necessary for training extremely deep networks.
 Quantitatively measures nonconvexity.
 S...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1712.09913#niz
http://www.shortscience.org/paper?bibtexKey=journals/corr/1712.09913#nizWed, 05 Dec 2018 13:58:02 07001703.06189journals/corr/1703.061892TURN TAP: Temporal Unit Regression Network for Temporal Action Proposalsshiyu## Temporal unit regression network
keyword: temporal action proposal; computing efficiency
**Summary**: In this paper, Jiyang et al designed a proposal generation and refinement network with high computation efficiency by reusing unit feature on coordinated regression and classification network. Especially, a new metric against temporal proposal called ARF is raised to meet 2 metric criteria: 1. evaluate different method on the same dataset efficiently. 2. capable to evaluate same method'...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1703.06189#daisy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1703.06189#daisyWed, 05 Dec 2018 12:03:51 07001601.02129journals/corr/1601.021292Temporal Action Localization in Untrimmed Videos via Multistage CNNsshiyu## Segmented SNN
**Summary**: this paper use 3stage 3D CNN to identify candidate proposals, recognize actions and localize temporal boundaries.
**Models**:
this network can be mainly divided into 3 parts: generate proposals, select proposal and refine temporal boundaries, and using NMS to remove redundant proposals.
1. generate multiscale(16,32,64,128,256.512) segment using sliding window with 75% overlap. high computing complexity!
2. network: Each stage of the threestage network is using...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1601.02129#daisy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1601.02129#daisyWed, 05 Dec 2018 12:03:16 07001806.02964journals/corr/1806.029642BSN: Boundary Sensitive Network for Temporal Action Proposal Generationshiyu## Boundary sensitive network
### **keyword**: action detection in video; accurate proposal
**Summary**: In order to generate precise temporal boundaries and improve recall with lesses proposals, Tianwei Lin et al use BSN which first combine temporal boundaries with high probability to form proposals and then select proposals by evaluating whether a proposal contains an action(confidence score+ boundary probability).
**Model**:
1. video feature encoding: use the twostream extractor to for...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.02964#daisy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.02964#daisyTue, 04 Dec 2018 03:11:41 07001807.00392journals/corr/1807.003922Gradient Reversal Against Discriminationameroyer Given some input data $x$ and attribute $a_p$, the task is to predict label $y$ from $x$ while making $a_p$ *protected*, in other words, such that the model predictions are invariant to changes in $a_p$.
* **Pros (+)**: Simple and intuitive idea, easy to train, naturally extended to protecting multiple attributes.
* **Cons ()**: Comparison to baselines could be more detailed / comprehensive, in particular the comparison to ALFR [4] which also relies on adversarial training.

## Pr...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.00392#ameroyer
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.00392#ameroyerMon, 03 Dec 2018 13:03:21 07001511.06984journals/corr/1511.069842Endtoend Learning of Action Detection from Frame Glimpses in Videosshiyu### **Keyword**: RNN, serialized model; nondifferentiable backpropogarion; action detection in video
**Abstract**: This paper uses an endtoend model which is a recurrent neural network trained by REINFORCE to directly predict the temporal bounds of actions. The intuition is that people will observe moments in video and decide where to look to predict when an action is occurring. After training, Serena et al manage to achieve the stateofart result by only observing 2% of the video frames....
http://www.shortscience.org/paper?bibtexKey=journals/corr/1511.06984#daisy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1511.06984#daisyMon, 03 Dec 2018 07:33:43 07001704.06228journals/corr/1704.062282Temporal Action Detection with Structured Segment Networksshiyu## Structured segmented network
### **key word**: action detection in video; computing complexity reduction; structurize proposal
**Abstract**: using a temporal action grouping scheme (TAG) to generate accurate proposals, using a structured pyramid to model the temporal structure of each action instance to tackle the issue that detected actions are not complete, using two classifiers to determine class and completeness and using a regressor for each category to further modify the temporal bou...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1704.06228#daisy
http://www.shortscience.org/paper?bibtexKey=journals/corr/1704.06228#daisyMon, 03 Dec 2018 07:30:44 07001809.11044journals/corr/1809.110444Relational Forward Models for MultiAgent LearningCodyWildOne of the dominant narratives of the deep learning renaissance has been the value of welldesigned inductive bias  structural choices that shape what a model learns. The biggest example of this can be found in convolutional networks, where models achieve a dramatic parameter reduction by having features maps learn local patterns, which can then be reused across the whole image. This is based on the prior belief that patterns in local images are generally locally contiguous, and so having feat...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.11044#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.11044#decodyngSat, 01 Dec 2018 01:13:47 07001710.08864journals/corr/1710.088643One pixel attack for fooling deep neural networksANIRUDH NJ
## **Keywords**
One pixel attack , adversarial examples , differential evolution , targeted and nontargeted attack

## **Summary**
1. **Introduction **
1. **Basics**
1. Deep learning methods are better than the traditional image processing techniques in most of the cases in computer vision domain.
1. "Adversarial examples" are specifically modified images with imperceptible perturbations that are classified wrong by the network.
1. **Goals of the paper**
...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1710.08864#anirudhnj
http://www.shortscience.org/paper?bibtexKey=journals/corr/1710.08864#anirudhnjFri, 30 Nov 2018 14:13:36 07001503.03832journals/corr/1503.038322FaceNet: A Unified Embedding for Face Recognition and ClusteringANIRUDH NJ
## Keywords
Tripletloss , face embedding , harmonic embedding

## Summary
### Introduction
**Goal of the paper**
A unified system is given for face verification , recognition and clustering.
Use of a 128 float pose and illumination invariant feature vector or embedding in the euclidean space.
* Face Verification : Same faces of the person gives feature vectors that have a very close L2 distance between them.
* Face recognition : Face recognition becomes a clustering task in the emb...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1503.03832#anirudhnj
http://www.shortscience.org/paper?bibtexKey=journals/corr/1503.03832#anirudhnjFri, 30 Nov 2018 10:42:36 07001312.6199journals/corr/1312.61993Intriguing properties of neural networksANIRUDH NJ### Keywords
Adversarial example , Perturbations

### Summary
##### Introduction
* Explain two properties of neural network that cause it to misclassify images and cause difficulty to get solid understanding of network.
1. Theoretical understanding of the individual high level unit of a network and a combination of these units or layers.
2. Understanding the continuity of input  output mapping space and the stability of the output wrt. the input.
* Performing a few experiments ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1312.6199#anirudhnj
http://www.shortscience.org/paper?bibtexKey=journals/corr/1312.6199#anirudhnjFri, 30 Nov 2018 10:41:26 07001810.11910journals/corr/1810.119102Learning to Learn without Forgetting By Maximizing Transfer and Minimizing InterferencewassnameCatastrophic forgetting is the tendency of an neural network to forget previously learned information when learning new information. This paper combats that by keeping a buffer of experience and applying metalearning to it. They call their new module Meta Experience Replay or MER.
How does this work? At each update they compute multiple possible updates to the model weights. One for the new batch of information and some more updates for batches of previous experience. Then they apply metalea...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.11910#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.11910#wassnameThu, 29 Nov 2018 22:10:31 07001811.06272journals/corr/1811.062722Woulda, Coulda, Shoulda: CounterfactuallyGuided Policy SearchCodyWildIt is a fact universally acknowledged that a reinforcement learning algorithm not in possession of a model must be in want of more data. Because they generally are. Joking aside, it is broadly understood that modelfree RL takes a lot of data to train, and, even when you can design them to use offpolicy trajectories, collecting data in the real environment might still be too costly. Under those conditions, we might want to learn a model of the environment and generate synthesized trajectories, ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06272#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06272#decodyngThu, 29 Nov 2018 07:14:06 07001811.06521journals/corr/1811.065212Reward learning from human preferences and demonstrations in AtariwassnameHow can humans help an agent perform at a task that has no clear reward? Imitation, demonstration, and preferences. This paper asks which combinations of imitation, demonstration, and preferences will best guide an agent in Atari games.
For example an agent that is playing Pong on the Atari, but can't access the score. You might help it by demonstrating your play style for a few hours. To help the agent further you are shown two short clips of it playing and you are asked to indicate which one,...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06521#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06521#wassnameThu, 29 Nov 2018 02:08:07 07001711.10485journals/corr/1711.104852AttnGAN: FineGrained Text to Image Generation with Attentional Generative Adversarial NetworksCodyWildThis paper feels a bit like watching a 90’s show, and everyone’s in denim and miniskirts, except it’s a 2017 ML paper, and everything uses attention. (I’ll say it again, ML years are like dog years, but more so). That said, that’s not a critique of the paper: finding clever ways to cobble together techniques for your application can be an important and valuable contribution. This paper addresses the problem of text to image generation: how to take a description of an image and generate...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.10485#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.10485#decodyngWed, 28 Nov 2018 04:55:26 07001807.03247journals/corr/1807.032474An Intriguing Failing of Convolutional Neural Networks and the CoordConv SolutionCodyWildThis is a paper where I keep being torn between the response of “this is so simple it’s brilliant; why haven’t people done it before,” and “this is so simple it’s almost tautological, and the results I’m seeing aren’t actually that surprising”. The basic observation this paper makes is one made frequently before, most recently to my memory by Geoff Hinton in his Capsule Net paper: sometimes the translation invariance of convolutional networks can be a bad thing, and lead to wor...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.03247#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.03247#decodyngTue, 27 Nov 2018 06:55:14 07001712.09913journals/corr/1712.099134Visualizing the Loss Landscape of Neural NetsCodyWildThis paper was a real delight to read, and even though I’m summarizing it here, I’d really encourage you, if you’re reading this, to read the paper itself, since I found it to be unusually clearly written. It tackles the problem of understanding how features of loss functions  these integral, yet arcane, objects defined in millions of parameterdimensions  impact model performance. Loss function analysis is generally a difficult area, since the number of dimensions and number of points n...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1712.09913#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1712.09913#decodyngMon, 26 Nov 2018 07:05:34 07001809.02861journals/corr/1809.028612On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning AttacksCodyWildThis paper focuses on the wellknown fact that adversarial examples are often transferable: that is, that an adversarial example created by optimizing loss on a surrogate model trained on similar data can often still induce increased loss on the true target model, though typically not to the same magnitude as an example optimized against the target itself. Its goal is to come up with clearer theoretical formulation for transferred examples, and more clearly understand what kinds of models transf...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.02861#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.02861#decodyngSun, 25 Nov 2018 01:36:26 07001806.11146journals/corr/1806.111463Adversarial Reprogramming of Neural NetworksCodyWildIn the literature of adversarial examples, there’s this (to me) constant question: is it the case that adversarial examples are causing the model to objectively make a mistake, or just displaying behavior that is deeply weird, and unintuitive relative to our sense of what these models “should” be doing. A lot of the former question seems to come down to arguing over about what’s technically “out of distribution”, which has an occasional angelsdancingonapin quality, but it’s pre...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.11146#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.11146#decodyngSat, 24 Nov 2018 05:14:44 07001807.09341journals/corr/1807.093412Learning Plannable Representations with Causal InfoGANCodyWildThis paper tries to solve the problem of how to learn systems that, given a starting state and a desired target, can earn the set of actions necessary to reach that target. The strong version of this problem requires a planning algorithm to learn a full set of actions to take the agent from state A to B. However, this is a difficult and complex task, and so this paper tries to address a relaxed version of this task: generating a set of “waypoint” observations between A and B, such that each ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.09341#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1807.09341#decodyngFri, 23 Nov 2018 06:49:58 07001810.08647journals/corr/1810.086472Intrinsic Social Motivation via Causal Influence in MultiAgent RLCodyWildThis paper builds very directly on the idea of “empowerment” as an intrinsic reward for RL agents. Where empowerment incentivizes agents to increase the amount of influence they’re able to have over the environment, “social influence,” this paper’s metric, is based on the degree which the actions of one agent influence the actions of other agents, within a multiagent setting. The goals between the two frameworks are a little different. The notion of “empowerment” is built around...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.08647#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.08647#decodyngWed, 21 Nov 2018 05:17:28 07001811.06032journals/corr/1811.060322Natural Environment Benchmarks for Reinforcement LearningwassnameThis paper proposed three new reinforcement learning tasks which involved dealing with images.
 Task 1: An agent crawls across a hidden image, revealing portions of it at each step. It must classify the image in the minimum amount of steps. For example classify the image as a cat after choosing to travel across the ears.
 Task 2: The agent crawls across a visible image to sit on it's target. For example a cat in a scene of pets.
 Task 3: The agent plays an Atari game where the background has...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06032#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.06032#wassnameWed, 21 Nov 2018 00:18:59 07001703.04908journals/corr/1703.049082Emergence of Grounded Compositional Language in MultiAgent PopulationsCodyWildThis paper performs a fascinating toy experiment, to try to see if something languagelike in structure can be effectively induced in a population of agents, if they are given incentives that promote it. In some sense, a lot of what they find “just makes sense,” but it’s still a useful proof of concept to show that it can be done.
The experiment they run takes place in a simple, twodimensional world, with a fixed number of landmarks (representing locations goals need to take place), and...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1703.04908#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1703.04908#decodyngMon, 19 Nov 2018 01:55:00 07001810.12162journals/corr/1810.121624ModelBased Active ExplorationCodyWildThis paper continues in the tradition of curiositybased models, which try to reward models for exploring novel parts of their environment, in the hopes this can intrinsically motivate learning. However, this paper argues that it’s insufficient to just treat novelty as an occasional bonus on top of a normal reward function, and that instead you should figure out a process that’s more specifically designed to increase novelty. Specifically: you should design a policy whose goal is to experien...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.12162#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.12162#decodyngSat, 17 Nov 2018 07:30:01 07001810.02274journals/corr/1810.022742Episodic Curiosity through ReachabilityCodyWildThis paper proposes a new curiositybased intrinsic reward technique that seeks to address one of the failure modes of previous curiosity methods. The basic idea of curiosity is that, often, exploring novel areas of an environment can be correlated with gaining reward within that environment, and that we can find ways to incentivize the former that don’t require a handdesigned reward function. This is appealing because many usefultolearn environments either lack inherent reward altogether, ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.02274#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.02274#decodyngFri, 16 Nov 2018 02:45:07 07001808.04355journals/corr/1808.043554LargeScale Study of CuriosityDriven LearningCodyWildI really enjoyed this paper  in addition to being a clean, fundamentally empirical work, it was also clearly written, and had some pretty delightful moments of quotable zen, which I’ll reference at the end. The paper’s goal is to figure out how far curiositydriven learning alone can take reinforcement learning systems, without the presence of an external reward signal. “Intrinsic” reward learning is when you construct a reward out of internal, inherent features of the environment, rath...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.04355#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.04355#decodyngThu, 15 Nov 2018 05:45:55 07001809.04474journals/corr/1809.044744Multitask Deep Reinforcement Learning with PopArtCodyWildThis paper posits that one of the central problems stopping multitask RL  that is, single models trained to perform multiple tasks well  from reaching better performance, is the inability to balance model resources and capacity between the different tasks the model is being asked to learn. Empirically, prior to this paper, multitask RL could reach ~50% of human accuracy on Atari and Deepmind Lab tasks. The fact that this is lower than human accuracy is actually somewhat less salient than the...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.04474#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1809.04474#decodyngTue, 13 Nov 2018 08:26:54 07001802.01561journals/corr/1802.015616IMPALA: Scalable Distributed DeepRL with Importance Weighted ActorLearner ArchitecturesCodyWildThis reinforcement learning paper starts with the constraints imposed an engineering problem  the need to scale up learning problems to operate across many GPUs  and ended up, as a result, needing to solve an algorithmic problem along with it.
In order to massively scale up their training to be able to train multiple problem domains in a single model, the authors of this paper implemented a system whereby many “worker” nodes execute trajectories (series of actions, states, and reward) an...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.01561#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1802.01561#decodyngMon, 12 Nov 2018 08:19:15 07001811.02549journals/corr/1811.025496Language GANs Falling ShortCodyWildThis paper’s highlevel goal is to evaluate how well GANtype structures for generating text are performing, compared to more traditional maximum likelihood methods. In the process, it zooms into the ways that the current set of metrics for comparing text generation fail to give a wellrounded picture of how models are performing.
In the old paradigm, of maximum likelihood estimation, models were both trained and evaluated on a maximizing the likelihood of each word, given the prior words in...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.02549#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.02549#decodyngSat, 10 Nov 2018 08:20:21 07001609.05473journals/corr/1609.054732SeqGAN: Sequence Generative Adversarial Nets with Policy GradientCodyWildGANs for images have made impressive progress in recent years, reaching everhigher levels of subjective realism. It’s also interesting to think about domains where the GAN architecture is less of a good fit. An example of one such domain is natural language.
As opposed to images, which are made of continuous pixel values, sentences are fundamentally sequences of discrete values: that is, words. In a GAN, when the discriminator makes its assessment of the realness of the image, the gradient ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1609.05473#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1609.05473#decodyngFri, 09 Nov 2018 05:29:01 07001811.01778journals/corr/1811.017784On the Evaluation of CommonSense Reasoning in Natural Language UnderstandingCodyWildI should say from the outset: I have a lot of fondness for this paper. It goes upstream of a lot of researchcommunity incentives: It’s not methodologically flashy, it’s not about beating the State of the Art with a bigger, better model (though, those papers certainly also have their place). The goal of this paper was, instead, to dive into a test set used to evaluate performance of models, and try to understand to what extent it’s really providing a rigorous test of what we want out of mo...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.01778#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1811.01778#decodyngWed, 07 Nov 2018 04:56:41 07001810.06682journals/corr/1810.066823Trellis Networks for Sequence ModelingCodyWildFor solving sequence modeling problems, recurrent architectures have been historically the most commonly used solution, but, recently, temporal convolution networks, especially with dilations to help capture longer term dependencies, have gained prominence. RNNs have theoretically much larger capacity to learn long sequences, but also have a lot of difficulty propagating signal forward through long chains of recurrent operations. This paper, which suggests the approach of Trellis Networks, place...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.06682#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.06682#decodyngMon, 05 Nov 2018 07:46:39 07001808.04891journals/corr/1808.048912Embedding GrammarsCodyWildThis paper is, on the whole, a refreshing jaunt into the applied side of the research word. It isn’t looking to solve a fundamental machine learning problem in some new way, but it does highlight and explore one potential beneficial application of a common and widely used technique: specifically, combining word embeddings with contextfree grammars (such as: regular expressions), to make the latter less rigid.
Regular expressions work by specifying specific hardcoded patterns of symbols, and...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.04891#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.04891#decodyngSun, 04 Nov 2018 06:52:09 07001810.13409journals/corr/1810.134094You May Not Need AttentionOfir PressAn attention mechanism and a separate encoder/decoder are two properties of almost every single neural translation model. The question asked in this paper is how far can we go without attention and without a separate encoder and decoder? And the answer is pretty far! The model presented preforms just as well as the attention model of Bahdanau on the four language directions that are studied in the paper.
The translation model presented in the paper is basically a simple recurrent language mod...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.13409#ofirpress
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.13409#ofirpressSat, 03 Nov 2018 09:31:24 06001810.04805journals/corr/1810.048058BERT: Pretraining of Deep Bidirectional Transformers for Language UnderstandingCodyWildThe last two years have seen a number of improvements in the field of language model pretraining, and BERT  Bidirectional Encoder Representations from Transformers  is the most recent entry into this canon. The general problem posed by language model pretraining is: can we leverage huge amounts of raw text, which aren’t labeled for any specific classification task, to help us train better models for supervised language tasks (like translation, question answering, logical entailment, etc)? Me...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.04805#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.04805#decodyngFri, 02 Nov 2018 06:43:01 06001810.06721journals/corr/1810.067213Optimizing Agent Behavior over Long Time Scales by Transporting ValuewassnameThis builds on the previous ["MERLIN"]() paper. First they introduce the RMA agent, which is a simplified version of MERLIN which uses model based RL and long term memory. They give the agent long term memory by letting it choose to save and load the agent's working memory (represented by the LSTM's hidden state).
Then they add credit assignment, similar to the RUDDER paper, to get the "Temporal Value Transport" (TVT) agent that can plan long term in the face of distractions. **The critical in...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.06721#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.06721#wassnameFri, 02 Nov 2018 01:45:04 060010.1101/2256642Prioritized memory access explains planning and hippocampal replaywassname**TL;DR:** There are 'place cells' in the hippopotamus that are fired when passing through a location. You can take a rat and measure how its cells are activated in a maze, then monitor neurons during planning, rest or sleep. You'll see patterns that show it's thinking of locations in order and focusing on interesting locations. This paper looks at how RL agents do 'prioritized experience replay' and compare it to place cells in animals. The authors do a RL simulation and *qualitatively* compare...
http://www.shortscience.org/paper?bibtexKey=10.1101/225664#wassname
http://www.shortscience.org/paper?bibtexKey=10.1101/225664#wassnameSun, 28 Oct 2018 04:05:27 06001806.07857journals/corr/1806.078573RUDDER: Return Decomposition for Delayed Rewardswassname[Summary by author /u/SirJAM_armedi]().
Math aside, the "big idea" of RUDDER is the following: We use an LSTM to predict the return of an episode. To do this, the LSTM will have to recognize what actually causes the reward (e.g. "shooting the gun in the right direction causes the reward, even if we get the reward only once the bullet hits the enemy after travelling along the screen"). We then use a salience method (e.g. LRP or integrated gradients) to get that information out of the LSTM, and r...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.07857#wassname
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.07857#wassnameSun, 28 Oct 2018 04:05:08 06001810.02334journals/corr/1810.023344Unsupervised Learning via MetaLearningCodyWildThis recent paper, a collaboration involving some of the authors of MAML, proposes an intriguing application of techniques developed in the field of meta learning to the problem of unsupervised learning  specifically, the problem of developing representations without labeled data, which can then be used to learn quickly from a small amount of labeled data. As a reminder, the idea behind meta learning is that you train models on multiple different tasks, using only a small amount of data from ea...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.02334#decodyng
http://www.shortscience.org/paper?bibtexKey=journals/corr/1810.02334#decodyngSat, 13 Oct 2018 03:41:42 060010.1109/83.9022912Active contours without edgesAnmol SharmaTypically, the energy minimization or snakes based object detection frameworks evolve a parametrized curve guided by some form of image gradient information. However due to heavy reliance on gradients, the approaches tend to fail in scenarios where this information is misleading or unavailable. This cripples the snake and renders it unusable as it gets stuck in a localminima away from the actual object. Moreover, the parametrized snake lacks the ability to model multiple evolving curves in a si...
http://www.shortscience.org/paper?bibtexKey=10.1109/83.902291#anmolsharma
http://www.shortscience.org/paper?bibtexKey=10.1109/83.902291#anmolsharmaWed, 10 Oct 2018 20:38:38 060010.1007/bf001335702Snakes: Active contour modelsAnmol SharmaLow level tasks such as edge, contour and line detection are an essential precursor to any downstream image analysis processes. However, most of the approaches targeting these problems work as isolated and autonomous entities, without using any highlevel image information such as context, global shapes, or userlevel input. This leads to errors that can further propagate through the pipeline without providing an opportunity for future correction. In order to address this problem, Kass et al. in...
http://www.shortscience.org/paper?bibtexKey=10.1007/bf00133570#anmolsharma
http://www.shortscience.org/paper?bibtexKey=10.1007/bf00133570#anmolsharmaWed, 10 Oct 2018 20:18:42 06001806.00340journals/corr/1806.003402Producing radiologistquality reports for interpretable artificial intelligenceTess BerthierThe paper presents a modelagnostic extension of deep learning classifiers based on a RNN with a visual attention mechanism for report generation.
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One of the most important points in this paper is not the model, but the dataset they itself: Luke OakdenRayner, one of the authors, is a radiologist and worked a lot to educate the public on current medical datasets ([chest xray blog post]()), how they are made and what are the problems associated with them. In this paper they used 50,363 f...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.00340#tessberthier
http://www.shortscience.org/paper?bibtexKey=journals/corr/1806.00340#tessberthierWed, 03 Oct 2018 20:51:21 06001512.03385journals/corr/HeZRS152Deep Residual Learning for Image RecognitionEddie SmolanskySources:


Summary:
 Took the first place in Imagenet 5 main tracks
 Revolution of depth: GoogLeNet was 22 layers with 6.7 top5 error,
Resnet is 152 layers with 3.57 top5 error
 Light on complexity: the 34 layer baseline is 18% of the FLOPs(multiplyadds) of VGG.
 Resnet 152 has lower time complexity than VGG16/19
 Extends well to detection and segmentation tasks
 Just stacking more layers gives worse performance. Why? In theory:
> A deeper model should not have
higher...
http://www.shortscience.org/paper?bibtexKey=journals/corr/HeZRS15#eddiesmolansky
http://www.shortscience.org/paper?bibtexKey=journals/corr/HeZRS15#eddiesmolanskySun, 23 Sep 2018 20:47:58 06001711.07618journals/corr/1711.076182$S^4$Net: Single Stage SalientInstance SegmentationEddie SmolanskyIt's like mask rcnn but for salient instances.
code will be available at .
They invented a layer "mask pooling" that they claim is better than ROI pooling and ROI align.
>As can be seen, our proposed
binary RoIMasking and ternary RoIMasking both outperform
RoIPool and RoIAlign in mAP0.7
. Specifically, our
ternary RoIMasking result improves the RoIAlign result by
around 2.5 points. This reflects that considering more context
information outside the proposals does help for salient
instance seg...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.07618#eddiesmolansky
http://www.shortscience.org/paper?bibtexKey=journals/corr/1711.07618#eddiesmolanskySun, 23 Sep 2018 20:39:52 06001705.07426journals/corr/1705.074262The Do's and Don'ts for CNNbased Face VerificationEddie Smolansky# Metadata
* **Title**: The Do’s and Don’ts for CNNbased Face Verification
* **Authors**: Ankan Bansal Carlos Castillo Rajeev Ranjan Rama Chellappa
UMIACS 
University of Maryland, College Park
* **Link**:
# Abstract
>Convolutional neural networks (CNN) have become the most sought after tools for addressing object recognition problems. Specifically, they have produced stateofthe art results for unconstrained face recognition and verification tasks. While the research community appears ...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1705.07426#eddiesmolansky
http://www.shortscience.org/paper?bibtexKey=journals/corr/1705.07426#eddiesmolanskySun, 23 Sep 2018 20:34:23 060010.21105/joss.006762OPEM : Open Source PEM Cell Simulation ToolSepand HaghighiModeling and simulation of protonexchange membrane fuel cells (PEMFC) may work as a powerful tool in the Research & development of renewable energy sources. The OpenSource PEMFC Simulation Tool (OPEM) is a modeling tool for evaluating the performance of proton exchange membrane fuel cells. This package is a combination of models (static/dynamic) that predict the optimum operating parameters of PEMFC. OPEM contained generic models that will accept as input, not only values of the operating vari...
http://www.shortscience.org/paper?bibtexKey=10.21105/joss.00676#sepandhaghighi
http://www.shortscience.org/paper?bibtexKey=10.21105/joss.00676#sepandhaghighiSat, 08 Sep 2018 10:00:26 06001808.07371journals/corr/1808.073715Everybody Dance NowOleksandr BailoThis paper presents a perframe imagetoimage translation system enabling copying of a motion of a person from a source video to a target person. For example, a source video might be a professional dancer performing complicated moves, while the target person is you. By utilizing this approach, it is possible to generate a video of you dancing as a professional. Check the authors' [video]() for the visual explanation.
**Data preparation**
The authors have manually recorded highresolution vide...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.07371#ukrdailo
http://www.shortscience.org/paper?bibtexKey=journals/corr/1808.07371#ukrdailoWed, 05 Sep 2018 07:15:05 06001804.02341journals/corr/1804.023412Compositional Obverter Communication Learning From Raw Visual InputBen BoginThis paper proposes a new training method for multiagent communication settings. They show the following referential game: A speaker sees an image of a 3d rendered object and describes it to a listener. The listener sees a different image and must decide if it is the same object as described by the speaker (has the same color and shape). The game can only be completed successfully if a communication protocol emerges that can express the color and shape the speaker sees.
The main contribution o...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1804.02341#benbogin
http://www.shortscience.org/paper?bibtexKey=journals/corr/1804.02341#benboginSun, 02 Sep 2018 21:04:11 060010.21105/joss.007292PyCM: Multiclass confusion matrix library in PythonSepand HaghighiPyCM is a multiclass confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for postclassification model evaluation that supports most classes and overall statistics parameters. PyCM is the swissarmy knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers.
http://www.shortscience.org/paper?bibtexKey=10.21105/joss.00729#sepandhaghighi
http://www.shortscience.org/paper?bibtexKey=10.21105/joss.00729#sepandhaghighiSat, 01 Sep 2018 22:20:36 060010.1111/cdep.122822From Babies to Robots: The Contribution of Developmental Robotics to Developmental PsychologyNatalia Diaz Rodriguez, PhDJoint summary from
Developmental robotics is the interdisciplinary approach to the autonomous design of behavioural and cognitive capabilities in artificial agents (robots) that takes direct inspiration from the developmental principles and mechanisms observed in the natural cognitive systems. It relies on a highly interdisciplinary effort of empirical developmental sciences such as developmental psychology, neuroscience, and comparative psychology, and computational and engineering disciplin...
http://www.shortscience.org/paper?bibtexKey=10.1111/cdep.12282#natalia
http://www.shortscience.org/paper?bibtexKey=10.1111/cdep.12282#nataliaThu, 23 Aug 2018 09:55:44 06001709.04326journals/corr/1709.043263Learning with OpponentLearning AwarenessmnoukhovNormal RL agents in multiagent scenarios treat their opponents as a static part of the environment, not taking into account the fact that other agents are learning as well. This paper proposes LOLA, a learning rule that should take the agency and learning of opponents into account by optimizing "return under one step lookahead of opponent learning"
So instead of optimizing under the current parameters of agent 1 and 2
$$V^1(\theta_i^1, \theta_i^2)$$
LOLA proposes to optimize taking into acc...
http://www.shortscience.org/paper?bibtexKey=journals/corr/1709.04326#mnoukhov
http://www.shortscience.org/paper?bibtexKey=journals/corr/1709.04326#mnoukhovMon, 13 Aug 2018 23:01:16 0600