240 citations · 353 across the 7 of their papers we have counts for
8 papers
Context-Aware Streaming Perception in Dynamic Environments
Gur-Eyal Sela, Ionel Gog, Justin Wong +9
Efficient vision works maximize accuracy under a latency budget. These works evaluate accuracy offline, one image at a time. However, real-time vision applications like autonomous…
Investigating Power laws in Deep Representation Learning
Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal +1
Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning. Access to task relevant labels at scale is often scarce or…
Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover +2
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. Existing appr…
Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal +2
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In th…
GANSynth: Adversarial Neural Audio Synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen +3
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregress…
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi +2
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework. The first problem is implicit bias present in the reward functions used…
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpre…
Recurrent Memory Addressing for describing videos
Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal +1
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. The proposed model natu…