SAMARTH SINHA

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

I am an undergraduate student in Computer Engineering at the University of Toronto and Vector Institute where I work with Animesh Garg and Roger Grosse 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.

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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!

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EXPERIENCE REPLAY WITH LIKELIHOOD-FREE IMPORTANCE WEIGHTS

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

arXiv

SAMARTH SINHA

RESEARCH INTERESTS

PUBLICATIONS / PRE-PRINTS

DIVA: DIVERSE VISUAL FEATURE AGGREGATION FOR DEEP METRIC LEARNING

CURRICULUM BY SMOOTHING

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

EXPERIENCE REPLAY WITH LIKELIHOOD-FREE IMPORTANCE WEIGHTS

REVISITING TRAINING STRATEGIES AND GENERALIZATION PERFORMANCE IN DEEP METRIC LEARNING

DIBS: DIVERSITY INDUCING INFORMATION BOTTLENECK IN MODEL ENSEMBLES

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

VARIATIONAL ADVERSARIAL ACTIVE LEARNING

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

MAX ENTROPY MODELS FOR FAST ADAPTATION