Subham Sekhar Sahoo

According to our database, Subham Sekhar Sahoo authored at least 15 papers between 2018 and 2025.

Collaborative distances:

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

2025

The Diffusion Duality.

Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, Volodymyr Kuleshov

CoRR, June, 2025

Esoteric Language Models.

Subham Sekhar Sahoo, Zhihan Yang, Yash Akhauri, Johnna Liu, Deepansha Singh, Zhoujun Cheng, Zhengzhong Liu, Eric Xing, John Thickstun, Arash Vahdat

CoRR, June, 2025

Remasking Discrete Diffusion Models with Inference-Time Scaling.

Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, Volodymyr Kuleshov

CoRR, March, 2025

Simple Guidance Mechanisms for Discrete Diffusion Models.

Yair Schiff, Subham Sekhar Sahoo, Hao Phung, Guanghan Wang, Sam Boshar, Hugo Dalla-torre, Bernardo P. de Almeida, Alexander M. Rush, Thomas Pierrot, Volodymyr Kuleshov

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

Marianne Arriola, Aaron Gokaslan, Justin T. Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Simple Guidance Mechanisms for Discrete Diffusion Models.

Yair Schiff, Subham Sekhar Sahoo, Hao Phung, Guanghan Wang, Sam Boshar, Hugo Dalla-torre, Bernardo P. de Almeida, Alexander Rush, Thomas Pierrot, Volodymyr Kuleshov

CoRR, 2024

Simple and Effective Masked Diffusion Language Models.

Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander M. Rush, Volodymyr Kuleshov

CoRR, 2024

Diffusion Models With Learned Adaptive Noise.

Subham S. Sahoo, Aaron Gokaslan, Christopher De Sa, Volodymyr Kuleshov

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Simple and Effective Masked Diffusion Language Models.

Subham S. Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander Rush, Volodymyr Kuleshov

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows.

Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov

Proceedings of the International Conference on Machine Learning, 2023

Backpropagation through Combinatorial Algorithms: Identity with Projection Works.

Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Training Neural Networks using SAT solvers.

Subham S. Sahoo

CoRR, 2022

Gradient Backpropagation Through Combinatorial Algorithms: Identity with Projection Works.

Subham Sekhar Sahoo, Marin Vlastelica, Anselm Paulus, Vít Musil, Volodymyr Kuleshov, Georg Martius

CoRR, 2022

2021

Scaling Symbolic Methods using Gradients for Neural Model Explanation.

Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley

Proceedings of the 9th International Conference on Learning Representations, 2021

2018

Learning Equations for Extrapolation and Control.

Subham S. Sahoo, Christoph H. Lampert, Georg Martius

Proceedings of the 35th International Conference on Machine Learning, 2018