Kehang Han

According to our database, Kehang Han authored at least 9 papers between 2017 and 2024.

Collaborative distances:

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

2024

Simplified and Generalized Masked Diffusion for Discrete Data.

Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias

CoRR, 2024

Transfer Learning for Text Diffusion Models.

Kehang Han, Kathleen Kenealy, Aditya Barua, Noah Fiedel, Noah Constant

CoRR, 2024

2023

Scaling Up Models and Data with t5x and seqio.

Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo

J. Mach. Learn. Res., 2023

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, Quan Vuong, Vincent Vanhoucke, Huong T. Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R. Sanketi, Grecia Salazar, Michael S. Ryoo, Krista Reymann, Kanishka Rao, Karl Pertsch, Igor Mordatch, Henryk Michalewski, Yao Lu, Sergey Levine, Lisa Lee, Tsang-Wei Edward Lee, Isabel Leal, Yuheng Kuang, Dmitry Kalashnikov, Ryan Julian, Nikhil J. Joshi, Alex Irpan, Brian Ichter, Jasmine Hsu, Alexander Herzog, Karol Hausman, Keerthana Gopalakrishnan, Chuyuan Fu, Pete Florence, Chelsea Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, Kehang Han

Proceedings of the Conference on Robot Learning, 2023

2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

CoRR, 2022

2021

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation.

Mengjie Liu, Alon Grinberg Dana, Matthew S. Johnson, Mark J. Goldman, Agnes Jocher, A. Mark Payne, Colin A. Grambow, Kehang Han, Nathan W. Yee, Emily J. Mazeau, Katrin Blondal, Richard H. West, C. Franklin Goldsmith, William H. Green Jr.

J. Chem. Inf. Model., 2021

Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift.

Kehang Han, Balaji Lakshminarayanan, Jeremiah Z. Liu

CoRR, 2021

2019

Scalability strategies for automated reaction mechanism generation.

Agnes Jocher, Nick M. Vandewiele, Kehang Han, Mengjie Liu, Connie W. Gao, Ryan J. Gillis, William H. Green Jr.

Comput. Chem. Eng., 2019

2017

On-the-fly pruning for rate-based reaction mechanism generation.

Kehang Han, William H. Green Jr., Richard H. West

Comput. Chem. Eng., 2017