
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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On Robustness and Transferability of Convolutional Neural Networks
Modern deep convolutional networks (CNNs) are often criticized for not g...
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SelfSupervised Learning of VideoInduced Visual Invariances
We propose a general framework for selfsupervised learning of transfera...
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Semantic Bottleneck Scene Generation
Coupling the highfidelity generation capabilities of labelconditional ...
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On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or selfsupervised representation l...
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Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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HighFidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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Recent Advances in AutoencoderBased Representation Learning
Learning useful representations with little or no supervision is a key c...
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
In recent years, the interest in unsupervised learning of disentangled r...
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SelfSupervised Generative Adversarial Networks
Conditional GANs are at the forefront of natural image synthesis. The ma...
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On Self Modulation for Generative Adversarial Networks
Training Generative Adversarial Networks (GANs) is notoriously challengi...
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The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative Adversarial Networks (GANs) are a class of deep generative mo...
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Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest...
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Deep Generative Models for DistributionPreserving Lossy Compression
We propose and study the problem of distributionpreserving lossy compre...
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Are GANs Created Equal? A LargeScale Study
Generative adversarial networks (GAN) are a powerful subclass of generat...
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OneShot Coresets: The Case of kClustering
Scaling clustering algorithms to massive data sets is a challenging task...
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Stochastic Submodular Maximization: The Case of Coverage Functions
Stochastic optimization of continuous objectives is at the heart of mode...
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Training Mixture Models at Scale via Coresets
How can we train a statistical mixture model on a massive data set? In t...
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Practical Coreset Constructions for Machine Learning
We investigate coresets  succinct, small summaries of large data sets ...
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Uniform Deviation Bounds for Unbounded Loss Functions like kMeans
Uniform deviation bounds limit the difference between a model's expected...
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Scalable and Distributed Clustering via Lightweight Coresets
Coresets are compact representations of data sets such that models train...
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Horizontally Scalable Submodular Maximization
A variety of largescale machine learning problems can be cast as instan...
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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Faced with massive data, is it possible to trade off (statistical) risk,...
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Lineartime Outlier Detection via Sensitivity
Outliers are ubiquitous in modern data sets. Distancebased techniques a...
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Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
Coresets are efficient representations of data sets such that models tra...
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