SAM SINHA

I am leading the AI lab at 1X with the goal of solving embodied AI for humanoids. We are hiring - read about the lab here!

Previously, I was on the founding team at Luma where I was one of the key researchers on Genie, Dream Machine, Ray 2, and Uni-1!

I am also technically a PhD student at the University of Toronto.

Previously, I have done research at FAIR London, MILA, UC Berkeley and the University of Toronto.

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PUBLICATIONS (old)

SPARSEPOSE: SPARSE-VIEW CAMERA POSE REGRESSION AND REFINEMENT

Samarth Sinha, Jason Zhang, Andrea Tagliasacchi, Igor Gilitschenski, David Lindell
CVPR 2023 (Poster)

COMMON PETS IN 3D: DYNAMIC NEW-VIEW SYNTHESIS OF REAL-LIFE DEFORMABLE CATEGORIES

Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein, Ignacio Rocco, Natalia Neverova, Andrea Vedaldi, David Novotny
CVPR 2023 (Highlight)

TEST: TEST-TIME SELF-TRAINING UNDER DISTRIBUTION SHIFT

Samarth Sinha, Peter Gehler, Francesco Locatello, Bernt Schiele

WACV 2023 (Poster)

KEYTR: KEYPOINT TRANSPORTER FOR 3D RECONSTRUCTION
OF DEFORMABLE OBJECTS IN VIDEOS

David Novotny, Ignacio Rocco, Samarth Sinha, Alexandre Carlier, Gael Kerchenbaum, Roman Shapovalov, Nikita Smetanin, Natalia Neverova, Ben Graham, Andrea Vedaldi

CVPR 2022 (Oral)

KOOPMAN Q-LEARNING: OFFLINE REINFORCEMENT LEARNING VIA SYMMETRIES OF DYNAMICS

Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara

ICML 2022 (poster)

STACKMIX: A COMPLIMENTARY MIX ALGORITHM

John Chen, Samarth Sinha, Anastasios Kyrillidis

UAI 2022 (Poster)

EXPERIENCE REPLAY WITH LIKELIHOOD-FREE IMPORTANCE WEIGHTS

Samarth Sinha*, Jiaming Song*, Animesh Garg, Stefano Ermon

L4DC 2022 (Oral)

CONSISTENCY REGULARIZATION FOR VARIATIONAL AUTOENCODERS

Samarth Sinha, Adji B. Dieng
NeurIPS 2021 (Poster)

CHARACTERIZING GENERALIZATION UNDER OUT-OF-DISTRIBUTION SHIFTS IN DEEP METRIC LEARNING

Karsten Roth*, Timo Milbich*, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Bjorn Ommer

NeurIPS 2021 (Poster)

S4RL: SURPRISINGLY SIMPLE SELF-SUPERVISION FOR REINFORCEMENT LEARNING

Samarth Sinha, Ajay Mandlekar, Animesh Garg

CoRL 2021 (Poster)

LEARNING BY WATCHING: PHYSICAL IMITATION OF MANIPULATION SKILLS FROM HUMAN VIDEOS

Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg

IROS 2021 (Poster)

DIBS: DIVERSITY INDUCING INFORMATION BOTTLENECK IN MODEL ENSEMBLES

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

AAAI 2021 (Poster)

CURRICULUM BY SMOOTHING

Samarth Sinha, Animesh Garg, Hugo Larochelle

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

NeurIPS 2020 (Poster)

D2RL: DEEP DENSE ARCHITECTURE FOR REINFORCEMENT LEARNING

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

NeurIPS Deep RL Workshop 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

ECCV 2020 (Poster)

REVISITING TRAINING STRATEGIES AND GENERALIZATION PERFORMANCE IN DEEP METRIC LEARNING

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

ICML 2020 (Poster)

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

Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena
ICML 2020 (Poster)

VARIATIONAL ADVERSARIAL ACTIVE LEARNING

Samarth Sinha*, Sayna Ebrahimi*, Trevor Darrell

ICCV 2019 (Oral)

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

Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, Zeynep Akata

CVPR 2019 (Poster)

Sam Sinha: Publications