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InfoGAN: Interpretable Representation Learning by ...

InfoGAN: Interpretable Representation Learning byInformation Maximizing Generative Adversarial NetsXi Chen , Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskever , Pieter Abbeel UC Berkeley, Department of Electrical Engineering and Computer Sciences OpenAIAbstractThis paper describes InfoGAN, an information-theoretic extension to the Gener-ative Adversarial Network that is able to learn disentangled representations in acompletely unsupervised manner. InfoGAN is a generative adversarial networkthat also maximizes the mutual information between a small subset of the latentvariables and the observation.

Another intriguing line of work consists of the ladder network [14], which has achieved spectacular results on a semi-supervised variant of the MNIST dataset. More recently, a model based on the ... Lake et al. [17] have been able to learn representations using probabilistic inference over Bayesian programs, which achieved convincing one-shot ...

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Semi-supervised Learning with Deep Generative Models

proceedings.neurips.cc

approximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...

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Learning Structured Output Representation using Deep ...

proceedings.neurips.cc

posterior inference. However, the parameters of the VAE can be estimated efficiently in the stochas-tic gradient variational Bayes (SGVB) [16] framework, where the variational lower bound of the log-likelihood is used as a surrogate objective function. The variational lower bound is written as: logp (x) = KL(q ˚(zjx)kp (zjx))+E q ˚(zjx) logq ...

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Generative Adversarial Imitation Learning

proceedings.neurips.cc

networks [8], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where

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Prototypical Networks for Few-shot Learning

proceedings.neurips.cc

˚: RD!RMwith learnable parameters ˚. Each prototype is the mean vector of the embedded support points belonging to its class: c k= 1 jS kj X (x i;y i)2S k f ˚(x i) (1) Given a distance function d: R M R ![0;+1), Prototypical Networks produce a distribution over classes for a query point x based on a softmax over distances to the prototypes ...

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Inductive Representation Learning on Large Graphs

proceedings.neurips.cc

node classification, clustering, and link prediction [11, 28, 35]. ... (e.g., citation data with text attributes, biological data with functional/molecular markers), our approach can also make use of structural features that are present in all graphs (e.g., node degrees). ... through theoretical analysis, that GraphSAGE is capable of learning ...

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Bootstrap Your Own Latent A New Approach to Self ...

proceedings.neurips.cc

mining strategies [14, 15] to retrieve the nega-tive pairs. In addition, their performance criti-cally depends on the choice of image augmenta- ... to prevent collapsing while preserving high performance. To prevent collapse, a straightforward solution …

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proceedings.neurips.cc

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proceedings.neurips.cc

pseudo-labels to learn visual representations. This method scales to large uncurated dataset and can be used for pre-training of supervised networks [7]. However, their formulation is not principled and recently, Asano et al. [2] show how to cast the pseudo-label assignment problem as an instance of the optimal transport problem.

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PyTorch: An Imperative Style, High-Performance Deep ...

proceedings.neurips.cc

Facebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com Abstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals

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proceedings.neurips.cc

task that is hard in theory, but sometimes easy in practice. Despite the NP-hardness of training general neural loss functions [3], simple gradient methods often find global minimizers (parameter configurations with zero or near-zero training loss), even when data and labels are randomized before training [43].

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