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04/09/2021
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size
Before deploying machine learning models it is critical to assess their ...
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04/06/2021
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
Meta and transfer learning are two successful families of approaches to ...
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10/27/2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
The idea behind the unsupervised learning of disentangled representation...
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10/22/2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for ...
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09/28/2020
Scalable Transfer Learning with Expert Models
Transfer of pre-trained representations can improve sample efficiency an...
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07/28/2020
A Commentary on the Unsupervised Learning of Disentangled Representations
The goal of the unsupervised learning of disentangled representations is...
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06/18/2020
What Do Neural Networks Learn When Trained With Random Labels?
We study deep neural networks (DNNs) trained on natural image data with ...
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06/10/2020
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
In recent years, on-policy reinforcement learning (RL) has been successf...
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02/26/2020
Predicting Neural Network Accuracy from Weights
We study the prediction of the accuracy of a neural network given only i...
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01/22/2020
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Uncertainty quantification for deep learning is a challenging open probl...
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12/24/2019
Large Scale Learning of General Visual Representations for Transfer
Transfer of pre-trained representations improves sample efficiency and s...
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12/05/2019
Self-Supervised Learning of Video-Induced Visual Invariances
We propose a general framework for self-supervised learning of transfera...
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11/26/2019
Semantic Bottleneck Scene Generation
Coupling the high-fidelity generation capabilities of label-conditional ...
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07/31/2019
On Mutual Information Maximization for Representation Learning
Many recent methods for unsupervised or self-supervised representation l...
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07/25/2019
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelera...
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07/01/2019
MULEX: Disentangling Exploitation from Exploration in Deep RL
An agent learning through interactions should balance its action selecti...
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06/19/2019
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value f...
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05/28/2019
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled a...
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05/26/2019
Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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03/06/2019
High-Fidelity Image Generation With Fewer Labels
Deep generative models are becoming a cornerstone of modern machine lear...
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02/21/2019
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
The softmax function on top of a final linear layer is the de facto meth...
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02/02/2019
Parameter-Efficient Transfer Learning for NLP
Fine-tuning large pre-trained models is an effective transfer mechanism ...
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12/03/2018
Towards Accurate Generative Models of Video: A New Metric & Challenges
Recent advances in deep generative models have lead to remarkable progre...
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11/29/2018
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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10/17/2018
A Case for Object Compositionality in Deep Generative Models of Images
Deep generative models seek to recover the process with which the observ...
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10/04/2018
Episodic Curiosity through Reachability
Rewards are sparse in the real world and most today's reinforcement lear...
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10/02/2018
On Self Modulation for Generative Adversarial Networks
Training Generative Adversarial Networks (GANs) is notoriously challengi...
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07/12/2018
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative Adversarial Networks (GANs) are a class of deep generative mo...
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07/09/2018
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approxima...
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06/13/2018
On Accurate Evaluation of GANs for Language Generation
Generative Adversarial Networks (GANs) are a promising approach to langu...
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05/31/2018
Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest...
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04/30/2018
Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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03/29/2018
MemGEN: Memory is All You Need
We propose a new learning paradigm called Deep Memory. It has the potent...
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03/22/2018
Gradient Descent Quantizes ReLU Network Features
Deep neural networks are often trained in the over-parametrized regime (...
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11/28/2017
Are GANs Created Equal? A Large-Scale Study
Generative adversarial networks (GAN) are a powerful subclass of generat...
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11/05/2017
Wasserstein Auto-Encoders
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for buil...
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05/23/2017
Better Text Understanding Through Image-To-Text Transfer
Generic text embeddings are successfully used in a variety of tasks. How...
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05/22/2017
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transp...
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01/09/2017