
What makes for good views for contrastive learning
Contrastive learning between multiple views of the data has recently ach...
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Using a thousand optimization tasks to learn hyperparameter search strategies
We present TaskSet, a dataset of tasks for use in training and evaluatin...
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Regularized Autoencoders via Relaxed Injective Probability Flow
Invertible flowbased generative models are an effective method for lear...
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WeaklySupervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
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On Implicit Regularization in βVAEs
While the impact of variational inference (VI) on posterior inference in...
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Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of vari...
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On Predictive Information Suboptimality of RNNs
Certain biological neurons demonstrate a remarkable capability to optima...
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Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Deploying machine learning systems in the real world requires both high ...
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Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
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On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many proble...
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Preventing Posterior Collapse with deltaVAEs
Due to the phenomenon of "posterior collapse," current latent variable g...
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An InformationTheoretic Analysis of Deep LatentVariable Models
We present an informationtheoretic framework for understanding tradeof...
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Continual Learning Through Synaptic Intelligence
While deep learning has led to remarkable advances across diverse applic...
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Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
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Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs...
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Categorical Reparameterization with GumbelSoftmax
Categorical variables are a natural choice for representing discrete str...
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Exponential expressivity in deep neural networks through transient chaos
We combine Riemannian geometry with the mean field theory of high dimens...
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On the Expressive Power of Deep Neural Networks
We propose a new approach to the problem of neural network expressivity,...
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Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
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The Fast Bilateral Solver
We present the bilateral solver, a novel algorithm for edgeaware smooth...
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Analyzing noise in autoencoders and deep networks
Autoencoders have emerged as a useful framework for unsupervised learnin...
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Ben Poole
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