Dipam Chakraborty

According to our database, Dipam Chakraborty authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

CoRR, 2024

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, Jianwei Yu, Rongzhi Gu, Roman A. Solovyev, Alexander L. Stempkovskiy, Tatiana Habruseva, Mikhail Sukhovei, Yuki Mitsufuji

CoRR, 2023

The Sound Demixing Challenge 2023 - Music Demixing Track.

Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco A. Martínez Ramírez, Wei-Hsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada P. Mohanty, Roman A. Solovyev, Alexander L. Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji

CoRR, 2023

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, Catherine E. Schretter, Keith Sheppard, Chao Sun, Param Uttarwar, Julian Morgan Wagner, Erik Werner, Joseph Parker, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy

Proceedings of the International Conference on Machine Learning, 2023

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

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, Param Uttarwar, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy

CoRR, 2022

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 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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

The CityLearn Challenge 2022: Overview, Results, and Lessons Learned.

Kingsley Nweye, Zoltán Nagy, Sharada Mohanty, Dipam Chakraborty, Siva Sankaranarayanan, Tianzhen Hong, Sourav Dey, Gregor Henze, Ján Drgona, Fangquan Lin, Wei Jiang, Hanwei Zhang, Zhongkai Yi, Jihai Zhang, Cheng Yang, Matthew Motoki, Sorapong Khongnawang, Michael Ibrahim, Abilmansur Zhumabekov, Daniel May, Zhihu Yang, Xiaozhuang Song, Han Zhang, Xiaoning Dong, Shun Zheng, Jiang Bian

Proceedings of the NeurIPS 2022 Competition Track, 2021

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, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

2020

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, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

2019

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