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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1547 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Chen, Xi and Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter

Neural Information Processing Systems Conference - 2016 via Local Bibsonomy

Keywords: dblp

Chen, Xi and Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter

Neural Information Processing Systems Conference - 2016 via Local Bibsonomy

Keywords: dblp

[link]
* Usually GANs transform a noise vector `z` into images. `z` might be sampled from a normal or uniform distribution. * The effect of this is, that the components in `z` are deeply entangled. * Changing single components has hardly any influence on the generated images. One has to change multiple components to affect the image. * The components end up not being interpretable. Ideally one would like to have meaningful components, e.g. for human faces one that controls the hair length and a categorical one that controls the eye color. * They suggest a change to GANs based on Mutual Information, which leads to interpretable components. * E.g. for MNIST a component that controls the stroke thickness and a categorical component that controls the digit identity (1, 2, 3, ...). * These components are learned in a (mostly) unsupervised fashion. ### How * The latent code `c` * "Normal" GANs parameterize the generator as `G(z)`, i.e. G receives a noise vector and transforms it into an image. * This is changed to `G(z, c)`, i.e. G now receives a noise vector `z` and a latent code `c` and transforms both into an image. * `c` can contain multiple variables following different distributions, e.g. in MNIST a categorical variable for the digit identity and a gaussian one for the stroke thickness. * Mutual Information * If using a latent code via `G(z, c)`, nothing forces the generator to actually use `c`. It can easily ignore it and just deteriorate to `G(z)`. * To prevent that, they force G to generate images `x` in a way that `c` must be recoverable. So, if you have an image `x` you must be able to reliable tell which latent code `c` it has, which means that G must use `c` in a meaningful way. * This relationship can be expressed with mutual information, i.e. the mutual information between `x` and `c` must be high. * The mutual information between two variables X and Y is defined as `I(X; Y) = entropy(X) - entropy(X|Y) = entropy(Y) - entropy(Y|X)`. * If the mutual information between X and Y is high, then knowing Y helps you to decently predict the value of X (and the other way round). * If the mutual information between X and Y is low, then knowing Y doesn't tell you much about the value of X (and the other way round). * The new GAN loss becomes `old loss - lambda * I(G(z, c); c)`, i.e. the higher the mutual information, the lower the result of the loss function. * Variational Mutual Information Maximization * In order to minimize `I(G(z, c); c)`, one has to know the distribution `P(c|x)` (from image to latent code), which however is unknown. * So instead they create `Q(c|x)`, which is an approximation of `P(c|x)`. * `I(G(z, c); c)` is then computed using a lower bound maximization, similar to the one in variational autoencoders (called "Variational Information Maximization", hence the name "InfoGAN"). * Basic equation: `LowerBoundOfMutualInformation(G, Q) = E[log Q(c|x)] + H(c) <= I(G(z, c); c)` * `c` is the latent code. * `x` is the generated image. * `H(c)` is the entropy of the latent codes (constant throughout the optimization). * Optimization w.r.t. Q is done directly. * Optimization w.r.t. G is done via the reparameterization trick. * If `Q(c|x)` approximates `P(c|x)` *perfectly*, the lower bound becomes the mutual information ("the lower bound becomes tight"). * In practice, `Q(c|x)` is implemented as a neural network. Both Q and D have to process the generated images, which means that they can share many convolutional layers, significantly reducing the extra cost of training Q. ### Results * MNIST * They use for `c` one categorical variable (10 values) and two continuous ones (uniform between -1 and +1). * InfoGAN learns to associate the categorical one with the digit identity and the continuous ones with rotation and width. * Applying Q(c|x) to an image and then classifying only on the categorical variable (i.e. fully unsupervised) yields 95% accuracy. * Sampling new images with exaggerated continuous variables in the range `[-2,+2]` yields sound images (i.e. the network generalizes well). * ![MNIST examples](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/InfoGAN__mnist.png?raw=true "MNIST examples") * 3D face images * InfoGAN learns to represent the faces via pose, elevation, lighting. * They used five uniform variables for `c`. (So two of them apparently weren't associated with anything sensible? They are not mentioned.) * 3D chair images * InfoGAN learns to represent the chairs via identity (categorical) and rotation or width (apparently they did two experiments). * They used one categorical variable (four values) and one continuous variable (uniform `[-1, +1]`). * SVHN * InfoGAN learns to represent lighting and to spot the center digit. * They used four categorical variables (10 values each) and two continuous variables (uniform `[-1, +1]`). (Again, a few variables were apparently not associated with anything sensible?) * CelebA * InfoGAN learns to represent pose, presence of sunglasses (not perfectly), hair style and emotion (in the sense of "smiling or not smiling"). * They used 10 categorical variables (10 values each). (Again, a few variables were apparently not associated with anything sensible?) * ![CelebA examples](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/InfoGAN__celeba.png?raw=true "CelebA examples") |

Embodied Question Answering

Abhishek Das and Samyak Datta and Georgia Gkioxari and Stefan Lee and Devi Parikh and Dhruv Batra

arXiv e-Print archive - 2017 via Local arXiv

Keywords: cs.CV, cs.AI, cs.CL, cs.LG

**First published:** 2017/11/30 (3 years ago)

**Abstract:** We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.
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Abhishek Das and Samyak Datta and Georgia Gkioxari and Stefan Lee and Devi Parikh and Dhruv Batra

arXiv e-Print archive - 2017 via Local arXiv

Keywords: cs.CV, cs.AI, cs.CL, cs.LG

[link]
This paper introduces a new AI task - Embodied Question Answering. The goal of this task for an agent is to be able to answer the question by observing the environment through a single egocentric RGB camera while being able to navigate inside the environment. The agent has 4 natural modules: https://i.imgur.com/6Mjidsk.png 1. **Vision**. 224x224 RGB images are processed by CNN to produce a fixed-size representation. This CNN is pretrained on pixel-to-pixel tasks such as RGB reconstruction, semantic segmentation, and depth estimation. 2. **Language**. Questions are encoded with 2-layer LSTMs with 128-d hidden states. Separate question encoders are used for the navigation and answering modules to capture important words for each module. 3. **Navigation** is composed of a planner (forward, left, right, and stop actions) and a controller that executes planner selected action for a variable number of times. The planner is LSTM taking hidden state, image representation, question, and previous action. Contrary, a controller is an MLP with 1 hidden layer which takes planner's hidden state, action from the planner, and image representation to execute an action or pass the lead back to the planner. 4. **Answering** module computes an image-question similarity of the last 5 frames via a dot product between image features (passed through an fc-layer to align with question features) and question encoding. This similarity is converted to attention weights via a softmax, and the attention-weighted image features are combined with the question features and passed through an answer classifier. Visually this process is shown in the figure below. https://i.imgur.com/LeZlSZx.png [Successful results](https://www.youtube.com/watch?v=gVj-TeIJfrk) as well as [failure cases](https://www.youtube.com/watch?v=4zH8cz2VlEg) are provided. Generally, this is very promising work which literally just scratches the surface of what is possible. There are several constraints which can be mitigated to push this field to more general outcomes. For example, use more general environments with more realistic graphics and broader set of questions and answers. |

Recurrent Batch Normalization

Cooijmans, Tim and Ballas, Nicolas and Laurent, César and Courville, Aaron

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

Cooijmans, Tim and Ballas, Nicolas and Laurent, César and Courville, Aaron

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

[link]
This paper describes how to apply the idea of batch normalization (BN) successfully to recurrent neural networks, specifically to LSTM networks. The technique involves the 3 following ideas: **1) Careful initialization of the BN scaling parameter.** While standard practice is to initialize it to 1 (to have unit variance), they show that this situation creates problems with the gradient flow through time, which vanishes quickly. A value around 0.1 (used in the experiments) preserves gradient flow much better. **2) Separate BN for the "hiddens to hiddens pre-activation and for the "inputs to hiddens" pre-activation.** In other words, 2 separate BN operators are applied on each contributions to the pre-activation, before summing and passing through the tanh and sigmoid non-linearities. **3) Use of largest time-step BN statistics for longer test-time sequences.** Indeed, one issue with applying BN to RNNs is that if the input sequences have varying length, and if one uses per-time-step mean/variance statistics in the BN transformation (which is the natural thing to do), it hasn't been clear how do deal with the last time steps of longer sequences seen at test time, for which BN has no statistics from the training set. The paper shows evidence that the pre-activation statistics tend to gradually converge to stationary values over time steps, which supports the idea of simply using the training set's last time step statistics. Among these ideas, I believe the most impactful idea is 1). The papers mentions towards the end that improper initialization of the BN scaling parameter probably explains previous failed attempts to apply BN to recurrent networks. Experiments on 4 datasets confirms the method's success. **My two cents** This is an excellent development for LSTMs. BN has had an important impact on our success in training deep neural networks, and this approach might very well have a similar impact on the success of LSTMs in practice. |

Relational Forward Models for Multi-Agent Learning

Andrea Tacchetti and H. Francis Song and Pedro A. M. Mediano and Vinicius Zambaldi and Neil C. Rabinowitz and Thore Graepel and Matthew Botvinick and Peter W. Battaglia

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.LG, cs.AI, cs.MA, stat.ML

**First published:** 2018/09/28 (2 years ago)

**Abstract:** The behavioral dynamics of multi-agent systems have a rich and orderly
structure, which can be leveraged to understand these systems, and to improve
how artificial agents learn to operate in them. Here we introduce Relational
Forward Models (RFM) for multi-agent learning, networks that can learn to make
accurate predictions of agents' future behavior in multi-agent environments.
Because these models operate on the discrete entities and relations present in
the environment, they produce interpretable intermediate representations which
offer insights into what drives agents' behavior, and what events mediate the
intensity and valence of social interactions. Furthermore, we show that
embedding RFM modules inside agents results in faster learning systems compared
to non-augmented baselines. As more and more of the autonomous systems we
develop and interact with become multi-agent in nature, developing richer
analysis tools for characterizing how and why agents make decisions is
increasingly necessary. Moreover, developing artificial agents that quickly and
safely learn to coordinate with one another, and with humans in shared
environments, is crucial.
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Andrea Tacchetti and H. Francis Song and Pedro A. M. Mediano and Vinicius Zambaldi and Neil C. Rabinowitz and Thore Graepel and Matthew Botvinick and Peter W. Battaglia

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.LG, cs.AI, cs.MA, stat.ML

[link]
One of the dominant narratives of the deep learning renaissance has been the value of well-designed 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 re-used across the whole image. This is based on the prior belief that patterns in local images are generally locally contiguous, and so having feature maps that focus only on small (and gradually larger) local areas is a good fit for that prior. This paper operates in a similar spirit, except its input data isn’t in the form of an image, but a graph: the social graph of multiple agents operating within a Multi Agent RL Setting. In some sense, a graph is just a more general form of a pixel image: where a pixel within an image has a fixed number of neighbors, which have fixed discrete relationships to it (up, down, left, right), nodes within graphs have an arbitrary number of nodes, which can have arbitrary numbers and types of attributes attached to that relationship. The authors of this paper use graph networks as a sort of auxiliary information processing system alongside a more typical policy learning framework, on tasks that require group coordination and knowledge sharing to complete successfully. For example, each agent might be rewarded based on the aggregate reward of all agents together, and, in the stag hunt, it might require collaborative effort by multiple agents to successfully “capture” a reward. Because of this, you might imagine that it would be valuable to be able to predict what other agents within the game are going to do under certain circumstances, so that you can shape your strategy accordingly. The graph network used in this model represents both agents and objects in the environment as nodes, which have attributes including their position, whether they’re available or not (for capture-able objects), and what their last action was. As best I can tell, all agents start out with directed connections going both ways to all other agents, and to all objects in the environment, with the only edge attribute being whether the players are on the same team, for competitive environments. Given this setup, the graph network works through a sort of “diffusion” of information, analogous to a message passing algorithm. At each iteration (analogous to a layer), the edge features pull in information from their past value and sender and receiver nodes, as well as from a “global feature”. Then, all of the nodes pull in information from their edges, and their own past value. Finally, this “global attribute” gets updated based on summations over the newly-updated node and edge information. (If you were predicting attributes that were graph-level attributes, this global attribute might be where you’d do that prediction. However, in this case, we’re just interested in predicting agent-level actions). https://i.imgur.com/luFlhfJ.png All of this has the effect of explicitly modeling agents as entities that both have information, and have connections to other entities. One benefit the authors claim of this structure is that it allows them more interpretability: when they “play out” the values of their graph network, which they call a Relational Forward Model or RFM, they observe edge values for two agents go up if those agents are about to collaborate on an action, and observe edge values for an agent and an object go up before that object is captured. Because this information is carefully shaped and structured, it makes it easier for humans to understand, and, in the tests the authors ran, appears to also help agents do better in collaborative games. https://i.imgur.com/BCKSmIb.png While I find graph networks quite interesting, and multi-agent learning quite interesting, I’m a little more uncertain about the inherent “graphiness” of this problem, since there aren’t really meaningful inherent edges between agents. One thing I am curious about here is how methods like these would work in situations of sparser graphs, or, places where the connectivity level between a node’s neighbors, and the average other node in the graph is more distinct. Here, every node is connected to every other node, so the explicit information localization function of graph networks is less pronounced. I might naively think that - to whatever extent the graph is designed in a way that captures information meaningful to the task - explicit graph methods would have an even greater comparative advantage in this setting. |

Collaborative Filtering for Implicit Feedback Datasets

Hu, Yifan and Koren, Yehuda and Volinsky, Chris

International Conference on Data Mining - 2008 via Local Bibsonomy

Keywords: collaborativfiltering, alternaterootsquare

Hu, Yifan and Koren, Yehuda and Volinsky, Chris

International Conference on Data Mining - 2008 via Local Bibsonomy

Keywords: collaborativfiltering, alternaterootsquare

[link]
This paper is about a recommendation system approach using collaborative filtering (CF) on implicit feedback datasets. The core of it is the minimization problem $$\min_{x_*, y_*} \sum_{u,i} c_{ui} (p_{ui} - x_u^T y_i)^2 + \underbrace{\lambda \left ( \sum_u || x_u ||^2 + \sum_i || y_i ||^2\right )}_{\text{Regularization}}$$ with * $\lambda \in [0, \infty[$ is a hyper parameter which defines how strong the model is regularized * $u$ denoting a user, $u_*$ are all user factors $x_u$ combined * $i$ denoting an item, $y_*$ are all item factors $y_i$ combined * $x_u \in \mathbb{R}^n$ is the latent user factor (embedding); $n$ is another hyper parameter. $n=50$ seems to be a reasonable choice. * $y_i \in \mathbb{R}^n$ is the latent item factor (embedding) * $r_{ui}$ defines the "intensity"; higher values mean user $u$ interacted more with item $i$ * $p_{ui} = \begin{cases}1 & \text{if } r_{ui} >0\\0 &\text{otherwise}\end{cases}$ * $c_{ui} := 1 + \alpha r_{ui}$ where $\alpha \in [0, \infty[$ is a hyper parameter; $\alpha =40$ seems to be reasonable In contrast, the standard matrix factoriation optimization function looks like this ([example](https://www.cs.cmu.edu/~mgormley/courses/10601-s17/slides/lecture25-mf.pdf)): $$\min_{x_*, y_*} \sum_{(u, i, r_{ui}) \in \mathcal{R}} {(r_{ui} - x_u^T y_i)}^2 + \underbrace{\lambda \left ( \sum_u || x_u ||^2 + \sum_i || y_i ||^2\right )}_{\text{Regularization}}$$ where * $\mathcal{R}$ is the set of all ratings $(u, i, r_{ui})$ - user $u$ has rated item $i$ with value $r_{ui} \in \mathbb{R}$ They use alternating least squares (ALS) to train this model. The prediction then is the dot product between the user factor and all item factors ([source](https://github.com/benfred/implicit/blob/master/implicit/recommender_base.pyx#L157-L176)) |

Network In Network

Min Lin and Qiang Chen and Shuicheng Yan

arXiv e-Print archive - 2013 via Local arXiv

Keywords: cs.NE, cs.CV, cs.LG

**First published:** 2013/12/16 (7 years ago)

**Abstract:** We propose a novel deep network structure called "Network In Network" (NIN)
to enhance model discriminability for local patches within the receptive field.
The conventional convolutional layer uses linear filters followed by a
nonlinear activation function to scan the input. Instead, we build micro neural
networks with more complex structures to abstract the data within the receptive
field. We instantiate the micro neural network with a multilayer perceptron,
which is a potent function approximator. The feature maps are obtained by
sliding the micro networks over the input in a similar manner as CNN; they are
then fed into the next layer. Deep NIN can be implemented by stacking mutiple
of the above described structure. With enhanced local modeling via the micro
network, we are able to utilize global average pooling over feature maps in the
classification layer, which is easier to interpret and less prone to
overfitting than traditional fully connected layers. We demonstrated the
state-of-the-art classification performances with NIN on CIFAR-10 and
CIFAR-100, and reasonable performances on SVHN and MNIST datasets.
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Min Lin and Qiang Chen and Shuicheng Yan

arXiv e-Print archive - 2013 via Local arXiv

Keywords: cs.NE, cs.CV, cs.LG

[link]
A paper in the intersection for Computer Vision and Machine Learning. They propose a method (network in network) to reduce parameters. Essentially, it boils down to a pattern of (conv with size > 1) -> (1x1 conv) -> (1x1 conv) -> repeat ## Datasets state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST ## Implementations * [Lasagne](https://github.com/Lasagne/Recipes/blob/master/modelzoo/cifar10_nin.py) |

Generative Adversarial Nets

Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron C. and Bengio, Yoshua

Neural Information Processing Systems Conference - 2014 via Local Bibsonomy

Keywords: dblp

Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron C. and Bengio, Yoshua

Neural Information Processing Systems Conference - 2014 via Local Bibsonomy

Keywords: dblp

[link]
* GANs are based on adversarial training. * Adversarial training is a basic technique to train generative models (so here primarily models that create new images). * In an adversarial training one model (G, Generator) generates things (e.g. images). Another model (D, discriminator) sees real things (e.g. real images) as well as fake things (e.g. images from G) and has to learn how to differentiate the two. * Neural Networks are models that can be trained in an adversarial way (and are the only models discussed here). ### How * G is a simple neural net (e.g. just one fully connected hidden layer). It takes a vector as input (e.g. 100 dimensions) and produces an image as output. * D is a simple neural net (e.g. just one fully connected hidden layer). It takes an image as input and produces a quality rating as output (0-1, so sigmoid). * You need a training set of things to be generated, e.g. images of human faces. * Let the batch size be B. * G is trained the following way: * Create B vectors of 100 random values each, e.g. sampled uniformly from [-1, +1]. (Number of values per components depends on the chosen input size of G.) * Feed forward the vectors through G to create new images. * Feed forward the images through D to create ratings. * Use a cross entropy loss on these ratings. All of these (fake) images should be viewed as label=0 by D. If D gives them label=1, the error will be low (G did a good job). * Perform a backward pass of the errors through D (without training D). That generates gradients/errors per image and pixel. * Perform a backward pass of these errors through G to train G. * D is trained the following way: * Create B/2 images using G (again, B/2 random vectors, feed forward through G). * Chose B/2 images from the training set. Real images get label=1. * Merge the fake and real images to one batch. Fake images get label=0. * Feed forward the batch through D. * Measure the error using cross entropy. * Perform a backward pass with the error through D. * Train G for one batch, then D for one (or more) batches. Sometimes D can be too slow to catch up with D, then you need more iterations of D per batch of G. ### Results * Good looking images MNIST-numbers and human faces. (Grayscale, rather homogeneous datasets.) * Not so good looking images of CIFAR-10. (Color, rather heterogeneous datasets.) ![Generated Faces](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/Generative_Adversarial_Networks__faces.jpg?raw=true "Generated Faces") *Faces generated by MLP GANs. (Rightmost column shows examples from the training set.)* ------------------------- ### Rough chapter-wise notes * Introduction * Discriminative models performed well so far, generative models not so much. * Their suggested new architecture involves a generator and a discriminator. * The generator learns to create content (e.g. images), the discriminator learns to differentiate between real content and generated content. * Analogy: Generator produces counterfeit art, discriminator's job is to judge whether a piece of art is a counterfeit. * This principle could be used with many techniques, but they use neural nets (MLPs) for both the generator as well as the discriminator. * Adversarial Nets * They have a Generator G (simple neural net) * G takes a random vector as input (e.g. vector of 100 random values between -1 and +1). * G creates an image as output. * They have a Discriminator D (simple neural net) * D takes an image as input (can be real or generated by G). * D creates a rating as output (quality, i.e. a value between 0 and 1, where 0 means "probably fake"). * Outputs from G are fed into D. The result can then be backpropagated through D and then G. G is trained to maximize log(D(image)), so to create a high value of D(image). * D is trained to produce only 1s for images from G. * Both are trained simultaneously, i.e. one batch for G, then one batch for D, then one batch for G... * D can also be trained multiple times in a row. That allows it to catch up with G. * Theoretical Results * Let * pd(x): Probability that image `x` appears in the training set. * pg(x): Probability that image `x` appears in the images generated by G. * If G is now fixed then the best possible D classifies according to: `D(x) = pd(x) / (pd(x) + pg(x))` * It is proofable that there is only one global optimum for GANs, which is reached when G perfectly replicates the training set probability distribution. (Assuming unlimited capacity of the models and unlimited training time.) * It is proofable that G and D will converge to the global optimum, so long as D gets enough steps per training iteration to model the distribution generated by G. (Again, assuming unlimited capacity/time.) * Note that these things are proofed for the general principle for GANs. Implementing GANs with neural nets can then introduce problems typical for neural nets (e.g. getting stuck in saddle points). * Experiments * They tested on MNIST, Toronto Face Database (TFD) and CIFAR-10. * They used MLPs for G and D. * G contained ReLUs and Sigmoids. * D contained Maxouts. * D had Dropout, G didn't. * They use a Parzen Window Estimate aka KDE (sigma obtained via cross validation) to estimate the quality of their images. * They note that KDE is not really a great technique for such high dimensional spaces, but its the only one known. * Results on MNIST and TDF are great. (Note: both grayscale) * CIFAR-10 seems to match more the texture but not really the structure. * Noise is noticeable in CIFAR-10 (a bit in TFD too). Comes from MLPs (no convolutions). * Their KDE score for MNIST and TFD is competitive or better than other approaches. * Advantages and Disadvantages * Advantages * No Markov Chains, only backprob * Inference-free training * Wide variety of functions can be incorporated into the model (?) * Generator never sees any real example. It only gets gradients. (Prevents overfitting?) * Can represent a wide variety of distributions, including sharp ones (Markov chains only work with blurry images). * Disadvantages * No explicit representation of the distribution modeled by G (?) * D and G must be well synchronized during training * If G is trained to much (i.e. D can't catch up), it can collapse many components of the random input vectors to the same output ("Helvetica") |

Generative adversarial networks uncover epidermal regulators and predict single cell perturbations

Arsham Ghahramani and Fiona M Watt and Nicholas M Luscombe

bioRxiv: The preprint server for biology - 2018 via Local CrossRef

Keywords:

Arsham Ghahramani and Fiona M Watt and Nicholas M Luscombe

bioRxiv: The preprint server for biology - 2018 via Local CrossRef

Keywords:

[link]
Lee et al. propose a variant of adversarial training where a generator is trained simultaneously to generated adversarial perturbations. This approach follows the idea that it is possible to “learn” how to generate adversarial perturbations (as in [1]). In this case, the authors use the gradient of the classifier with respect to the input as hint for the generator. Both generator and classifier are then trained in an adversarial setting (analogously to generative adversarial networks), see the paper for details. [1] Omid Poursaeed, Isay Katsman, Bicheng Gao, Serge Belongie. Generative Adversarial Perturbations. ArXiv, abs/1712.02328, 2017. |

Convex Learning with Invariances

Teo, Choon Hui and Globerson, Amir and Roweis, Sam T. and Smola, Alexander J.

Neural Information Processing Systems Conference - 2007 via Local Bibsonomy

Keywords: dblp

Teo, Choon Hui and Globerson, Amir and Roweis, Sam T. and Smola, Alexander J.

Neural Information Processing Systems Conference - 2007 via Local Bibsonomy

Keywords: dblp

[link]
Teo 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 different algorithms for minimizing this loss are proposed and discussed. The framework is evaluated on MNIST. However, considering error rate, the approach does not outperform data augmentation significantly (here, data augmentation means adding “virtual, i.e., transformed, samples to the training set). However, the proposed method seems to find sparser solutions, requiring less support vectors. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/). |

MuProp: Unbiased Backpropagation for Stochastic Neural Networks

Gu, Shixiang and Levine, Sergey and Sutskever, Ilya and Mnih, Andriy

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

Gu, Shixiang and Levine, Sergey and Sutskever, Ilya and Mnih, Andriy

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

[link]
This paper presents a method for training feed-forward neural networks with stochastic hidden units (e.g. sigmoid belief networks), to optimize the expectation (over the stochastic units) of some arbitrary loss function. While the proposed method is applicable to any type of stochastic units, it is most interesting for the case of discrete stochastic units, since the reparametrization trick of variational autoencoders cannot be applied to backprop through the sampling step. In short, the method builds on the likelihood ratio method (of which REINFORCE is a special case) and proposes a baseline (also known as control variate) which, according to the authors, is such that an unbiased gradient is obtained. Specifically, the baseline corresponds to the first-order Taylor expansion of the loss function around some deterministic value of the hidden units (x̄) that doesn't depend on the stochastic hidden units (noted x in the paper). For a likelihood ratio method to be unbiased, it is required that the expectation of the baseline (times the gradient of the model's log distribution) with respect to the model's distribution be tractable. For the proposed baseline, it can be shown that computing this expectation requires the gradient of the mean (μ) of each stochastic unit in the network with respect to each parameter. The key idea behind the proposed method is that 1) an estimate of this expectation can be obtained simply using mean-field and 2) since mean-field is estimated by a feedforward deterministic pass over the network, it is thus possible to compute the gradients of μ by backpropagation through the mean-field pass (hence the name of the method, MuProp). Experiments show that this method converges much faster than previously proposed unbiased methods and often performs better. Experiments also show that the method obtains competitive performance compared to biased methods (such as the "straight through" method). |

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