Moksh Jain

According to our database, Moksh Jain authored at least 21 papers between 2019 and 2023.

Collaborative distances:

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

2023

DEUP: Direct Epistemic Uncertainty Prediction.

Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

Trans. Mach. Learn. Res., 2023

PhyloGFN: Phylogenetic inference with generative flow networks.

Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio

CoRR, 2023

Amortizing intractable inference in large language models.

Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin

CoRR, 2023

Pre-Training and Fine-Tuning Generative Flow Networks.

Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio

CoRR, 2023

Thompson sampling for improved exploration in GFlowNets.

Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, Yoshua Bengio

CoRR, 2023

BatchGFN: Generative Flow Networks for Batch Active Learning.

Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

CoRR, 2023

Multi-Fidelity Active Learning with GFlowNets.

Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

CoRR, 2023

GFlowNets for AI-Driven Scientific Discovery.

Moksh Jain, Tristan Deleu, Jason S. Hartford, Cheng-Hao Liu, Alex Hernández-García, Yoshua Bengio

CoRR, 2023

Stochastic Generative Flow Networks.

Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability.

Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin

Proceedings of the International Conference on Machine Learning, 2023

GFlowOut: Dropout with Generative Flow Networks.

Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

Multi-Objective GFlowNets.

Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio

Proceedings of the International Conference on Machine Learning, 2023

GFlowNet-EM for Learning Compositional Latent Variable Models.

Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

2022

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions.

Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio

CoRR, 2022

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection.

Bonaventure F. P. Dossou, Dianbo Liu, Xu Ji, Moksh Jain, Almer M. van der Sloot, Roger Palou, Michael Tyers, Yoshua Bengio

CoRR, 2022

Trajectory balance: Improved credit assignment in GFlowNets.

Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Biological Sequence Design with GFlowNets.

Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2022

2021

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation.

Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

DROCC: Deep Robust One-Class Classification.

Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain

Proceedings of the 37th International Conference on Machine Learning, 2020

Improving Convergence in IRGAN with PPO.

Moksh Jain, S. Sowmya Kamath

Proceedings of the CoDS-COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD, 2020

2019

Proximal Policy Optimization for Improved Convergence in IRGAN.

Moksh Jain, Sowmya Kamath S.

CoRR, 2019