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Found 11 papers, 5 papers with code
GANSynth: Adversarial Neural Audio Synthesis
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.
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Language Modelling
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Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework.
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
We introduce the "inverse bandit" problem of estimating the rewards of a multi-armed bandit instance from observing the learning process of a low-regret demonstrator.
Context-Aware Streaming Perception in Dynamic Environments
These works evaluate accuracy offline, one image at a time.
Recurrent Memory Addressing for describing videos
In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models.
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks.
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games
Acquiring language provides a ubiquitous mode of communication, across humans and robots.
Investigating Power laws in Deep Representation Learning
Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with self-supervised learning (SSL).
Attribute Diversity Determines the Systematicity Gap in VQA
The degree to which neural networks can generalize to new combinations of familiar concepts, and the conditions under which they are able to do so, has long been an open question.
Addressing Sample Inefficiency in Multi-View Representation Learning
In this work, we provide theoretical insights on the implicit bias of the BarlowTwins and VICReg loss that can explain these heuristics and guide the development of more principled recommendations.
