Search Results for author: Kumar Krishna Agrawal

Found 9 papers, 5 papers with code

Investigating Power laws in Deep Representation Learning

no code implementations11 Feb 2022 Arna Ghosh, Arnab Kumar Mondal, Kumar Krishna Agrawal, Blake Richards

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).

Representation Learning Scene Recognition +1

Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits

1 code implementation28 Jun 2021 Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady

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.

Experimental Design

Discrete Flows: Invertible Generative Models of Discrete Data

2 code implementations NeurIPS 2019 Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole

While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.

Language Modelling

GANSynth: Adversarial Neural Audio Synthesis

5 code implementations ICLR 2019 Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts

Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.

Audio Generation

Towards Mixed Optimization for Reinforcement Learning with Program Synthesis

no code implementations1 Jul 2018 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.

Program Repair reinforcement-learning +1

Recurrent Memory Addressing for describing videos

no code implementations20 Nov 2016 Arnav Kumar Jain, Abhinav Agarwalla, Kumar Krishna Agrawal, Pabitra Mitra

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.

Video Captioning

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