Search Results for author:
Found 25 papers, 14 papers with code
Ignorance is Bliss: Robust Control via Information Gating
We propose \textit{information gating} as a way to learn parsimonious representations that identify the minimal information required for a task.
Pretraining Representations for Data-Efficient Reinforcement Learning
Data efficiency is a key challenge for deep reinforcement learning.
Ranked #3 on
Atari Games 100k
on Atari 100k
(using extra training data)
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning
Data efficiency poses a major challenge for deep reinforcement learning.
Decomposing Mutual Information for Representation Learning
In this paper, we transform each view into a set of subviews and then decompose the original MI bound into a sum of bounds involving conditional MI between the subviews.
Representation Learning with Video Deep InfoMax
DeepInfoMax (DIM) is a self-supervised method which leverages the internal structure of deep networks to construct such views, forming prediction tasks between local features which depend on small patches in an image and global features which depend on the whole image.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
Ranked #9 on
Atari Games 100k
on Atari 100k
Deep Reinforcement and InfoMax Learning
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving and adapting to new RL problems.
Learning Representations by Maximizing Mutual Information Across Views
Following our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider.
Ranked #22 on
Image Classification
on STL-10
Learning Invariances for Policy Generalization
While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks.
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data.
Deep Reinforcement Learning that Matters
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).
Variational Generative Stochastic Networks with Collaborative Shaping
We develop an approach to training generative models based on unrolling a variational auto-encoder into a Markov chain, and shaping the chain's trajectories using a technique inspired by recent work in Approximate Bayesian computation.
Learning Algorithms for Active Learning
We introduce a model that learns active learning algorithms via metalearning.
Machine Comprehension by Text-to-Text Neural Question Generation
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.
Calibrating Energy-based Generative Adversarial Networks
In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimal.
Ranked #17 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
An Architecture for Deep, Hierarchical Generative Models
We present an architecture which lets us train deep, directed generative models with many layers of latent variables.
Towards Information-Seeking Agents
We develop a general problem setting for training and testing the ability of agents to gather information efficiently.
NewsQA: A Machine Comprehension Dataset
We present NewsQA, a challenging machine comprehension dataset of over 100, 000 human-generated question-answer pairs.
Natural Language Generation in Dialogue using Lexicalized and Delexicalized Data
Natural language generation plays a critical role in spoken dialogue systems.
Iterative Alternating Neural Attention for Machine Reading
We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document.
Ranked #3 on
Question Answering
on Children's Book Test
(Accuracy-NE metric)
Testing Visual Attention in Dynamic Environments
We investigate attention as the active pursuit of useful information.
Data Generation as Sequential Decision Making
We connect a broad class of generative models through their shared reliance on sequential decision making.
Learning with Pseudo-Ensembles
We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process.
Representation as a Service
Consider a Machine Learning Service Provider (MLSP) designed to rapidly create highly accurate learners for a never-ending stream of new tasks.
