SAMARTH SINHA

samarth [dot] sinha [at] mail [dot] utoronto [dot] ca

I am a final year undergraduate student in Computer Engineering at the University of Toronto and Vector Institute where I work with Animesh Garg on the intersection of machine learning and reinforcement learning.

Previously, I was a visiting student at MILA working closely with Anirudh Goyal, Hugo Larochelle and Yoshua Bengio; I was also a visitor at UC Berkeley, working with Trevor Darrell and Zeynep Akata. I have also been very fortunate to work at some amazing startups: Aurora, DeepMap, Concured, Nanoleaf.

I spent my high school summers working with Alex Lvovsky working on Quantum Optics and with Guenther Ruhe working on Software Engineering at the University of Calgary.

RESEARCH INTERESTS

My research interests span across Deep Learning and (Deep) Reinforcement Learning. I am interested in training models and agents that can learn generalizable representations from minimal supervision.

I have had the privilege to work with some amazing collaborators, and I am always interested in chatting about new problems. If you find anything interesting on the website, please get in touch!

PUBLICATIONS / PRE-PRINTS

D2RL: DEEP DENSE ARCHITECTURES FOR REINFORCEMENT LEARNING

Samarth Sinha*, Homanga Bharadhwaj*, Aravind Srinivas, Animesh Garg

UNIFORM PRIOR FOR DATA-EFFICIENT TRANSFER

Samarth Sinha*, Karsten Roth*, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle Animesh Garg (2020)

DIVA: DIVERSE VISUAL FEATURE AGGREGATION FOR DEEP METRIC LEARNING

Karsten Roth*, Timo Milbich*, Homanga Bharadhwaj, Samarth Sinha, Joseph Paul Cohen, Bjorn Ommer, Yoshua Bengio (2020)

ECCV 2020 (Poster)

CURRICULUM BY SMOOTHING

Samarth Sinha, Animesh Garg, Hugo Larochelle (2020)

NeurIPS 2020 (Spotlight)

TOP-K TRAINING OF GANS: IMPROVING GAN PERFORMANCE BY THROWING AWAY BAD SAMPLES

Samarth Sinha*, Zhengli Zhao*, Anirudh Goyal, Colin Raffel, Augustus Odena (2020)

NeurIPS 2020 (Poster)

EXPERIENCE REPLAY WITH LIKELIHOOD-FREE IMPORTANCE WEIGHTS

Samarth Sinha*, Jiaming Song*, Animesh Garg, Stefano Ermon (2020)

REVISITING TRAINING STRATEGIES AND GENERALIZATION PERFORMANCE IN DEEP METRIC LEARNING

Karsten Roth*, Timo Milbich*, Samarth Sinha, Prateek Gupta, Bjoern Ommer, Joseph Paul Cohen (2020)

ICML 2020 (Poster)

DIBS: DIVERSITY INDUCING INFORMATION BOTTLENECK IN MODEL ENSEMBLES

Samarth Sinha*, Homanga Bharadhwaj*, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti (2020)

AAAI 2021 (Poster)

SMALL-GAN: SPEEDING UP GAN TRAINING USING CORE-SETS

Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena (2019)

ICML 2020 (Poster)

VARIATIONAL ADVERSARIAL ACTIVE LEARNING

Samarth Sinha*, Sayna Ebrahimi*, Trevor Darrell (2019)

ICCV 2019 (Oral)

GENERALIZED ZERO- AND FEW-SHOT LEARNING VIA ALIGNED VARIATIONAL AUTOENCODERS

Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata (2018)

CVPR 2019 (Poster)