
-
VIB is Half Bayes
In discriminative settings such as regression and classification there a...
read it
-
Non-saturating GAN training as divergence minimization
Non-saturating generative adversarial network (GAN) training is widely u...
read it
-
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
Much as replacing hand-designed features with learned functions has revo...
read it
-
What makes for good views for contrastive learning
Contrastive learning between multiple views of the data has recently ach...
read it
-
Using a thousand optimization tasks to learn hyperparameter search strategies
We present TaskSet, a dataset of tasks for use in training and evaluatin...
read it
-
Regularized Autoencoders via Relaxed Injective Probability Flow
Invertible flow-based generative models are an effective method for lear...
read it
-
Weakly-Supervised Disentanglement Without Compromises
Intelligent agents should be able to learn useful representations by obs...
read it
-
On Implicit Regularization in β-VAEs
While the impact of variational inference (VI) on posterior inference in...
read it
-
Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of vari...
read it
-
On Predictive Information Sub-optimality of RNNs
Certain biological neurons demonstrate a remarkable capability to optima...
read it
-
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Deploying machine learning systems in the real world requires both high ...
read it
-
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
read it
-
On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many proble...
read it
-
Preventing Posterior Collapse with delta-VAEs
Due to the phenomenon of "posterior collapse," current latent variable g...
read it
-
An Information-Theoretic Analysis of Deep Latent-Variable Models
We present an information-theoretic framework for understanding trade-of...
read it
-
Continual Learning Through Synaptic Intelligence
While deep learning has led to remarkable advances across diverse applic...
read it
-
Survey of Expressivity in Deep Neural Networks
We survey results on neural network expressivity described in "On the Ex...
read it
-
Unrolled Generative Adversarial Networks
We introduce a method to stabilize Generative Adversarial Networks (GANs...
read it
-
Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete str...
read it
-
Exponential expressivity in deep neural networks through transient chaos
We combine Riemannian geometry with the mean field theory of high dimens...
read it
-
On the Expressive Power of Deep Neural Networks
We propose a new approach to the problem of neural network expressivity,...
read it
-
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
read it
-
The Fast Bilateral Solver
We present the bilateral solver, a novel algorithm for edge-aware smooth...
read it
-
Analyzing noise in autoencoders and deep networks
Autoencoders have emerged as a useful framework for unsupervised learnin...
read it