Hardware Acceleration of Computer Vision and Deep Learning Algorithms on the Edge using OpenCL. - Bakshree Mishra; Dipam Chakraborty; Srajudheen Makkadayil; Saurabh D. Patil; Bhaskar Nallani (EAI Endorsed Trans. Cloud Syst., 2019)

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. - Sharada P. Mohanty; Jyotish Poonganam; Adrien Gaidon; Andrey Kolobov; Blake Wulfe; Dipam Chakraborty; Grazvydas Semetulskis; João Schapke; Jonas Kubilius; Jurgis Pasukonis; Linas Klimas; Matthew J. Hausknecht; et al. (NeurIPS (Competition and Demos), 2020)

Insights From the NeurIPS 2021 NetHack Challenge. - Eric Hambro; Sharada P. Mohanty; Dmitrii Babaev; Minwoo Byeon; Dipam Chakraborty; Edward Grefenstette; Minqi Jiang; DaeJin Jo; Anssi Kanervisto; Jongmin Kim 006; Sungwoong Kim; Robert Kirk; et al. (NeurIPS (Competition and Demos), 2021)

The CityLearn Challenge 2022: Overview, Results, and Lessons Learned. - Kingsley Nweye; Zoltán Nagy 002; Sharada P. Mohanty; Dipam Chakraborty; Siva Sankaranarayanan; Tianzhen Hong; Sourav Dey; Gregor Henze; Ján Drgona; Fangquan Lin; Wei Jiang 027; Hanwei Zhang 002; et al. (NeurIPS (Competition and Demos), 2021)

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. - Jennifer J. Sun; Tomomi Karigo; Dipam Chakraborty; Sharada P. Mohanty; Benjamin Wild; Quan Sun; Chen Chen; David J. Anderson; Pietro Perona; Yisong Yue; Ann Kennedy (NeurIPS Datasets and Benchmarks, 2021)

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. - Jennifer J. Sun; Tomomi Karigo; Dipam Chakraborty; Sharada P. Mohanty; David J. Anderson; Pietro Perona; Yisong Yue; Ann Kennedy (CoRR, 2021)

The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior. - Jennifer J. Sun; Andrew Ulmer; Dipam Chakraborty; Brian Geuther; Edward Hayes; Heng Jia; Vivek Kumar; Zachary Partridge; Alice Robie; Catherine E. Schretter; Chao Sun; Keith Sheppard; et al. (CoRR, 2022)

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms. - Shengyi Huang; Rousslan Fernand Julien Dossa; Chang Ye; Jeff Braga; Dipam Chakraborty; Kinal Mehta; João G. M. Araújo (J. Mach. Learn. Res., 2022)

MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior. - Jennifer J. Sun; Markus Marks; Andrew Wesley Ulmer; Dipam Chakraborty; Brian Geuther; Edward Hayes; Heng Jia; Vivek Kumar; Sebastian Oleszko; Zachary Partridge; Milan Peelman; Alice Robie; et al. (ICML, 2023)

The Sound Demixing Challenge 2023 - Cinematic Demixing Track. - Stefan Uhlich; Giorgio Fabbro; Masato Hirano; Shusuke Takahashi; Gordon Wichern; Jonathan Le Roux; Dipam Chakraborty; Sharada P. Mohanty; Kai Li; Yi Luo 004; Jianwei Yu 001; Rongzhi Gu; et al. (CoRR, 2023)

Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence. - Rakshit Trivedi; Akbir Khan; Jesse Clifton; Lewis Hammond; Edgar A. Duéñez-Guzmán; Dipam Chakraborty; John P. Agapiou; Jayd Matyas; Alexander Sasha Vezhnevets; Barna Pásztor; Yunke Ao; Omar G. Younis; et al. (NeurIPS, 2024)

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning. - Shengyi Huang; Quentin Gallouédec; Florian Felten; Antonin Raffin; Rousslan Fernand Julien Dossa; Yanxiao Zhao; Ryan Sullivan; Viktor Makoviychuk; Denys Makoviichuk; Mohamad H. Danesh; Cyril Roumégous; Jiayi Weng; et al. (CoRR, 2024)

The Sound Demixing Challenge 2023 - Music Demixing Track. - Giorgio Fabbro; Stefan Uhlich; Chieh-Hsin Lai; Woosung Choi; Marco A. Martínez Ramírez; Wei-Hsiang Liao 001; Igor Gadelha; Geraldo Ramos; Eddie Hsu; Hugo Rodrigues; Fabian-Robert Stöter; Alexandre Défossez; et al. (Trans. Int. Soc. Music. Inf. Retr., 2024)

The Sound Demixing Challenge 2023 - Cinematic Demixing Track. - Stefan Uhlich; Giorgio Fabbro; Masato Hirano; Shusuke Takahashi; Gordon Wichern; Jonathan Le Roux; Dipam Chakraborty; Sharada Mohanty; Kai Li; Yi Luo 004; Jianwei Yu 001; Rongzhi Gu; et al. (Trans. Int. Soc. Music. Inf. Retr., 2024)