Using Chinese Glyphs for Named Entity Recognition. - Arijit Sehanobish; Chan Hee Song (CoRR, 2019)

Using Chinese Glyphs for Named Entity Recognition (Student Abstract). - Chan Hee Song; Arijit Sehanobish (AAAI, 2020)

Disease state prediction from single-cell data using graph attention networks. - Neal G. Ravindra; Arijit Sehanobish; Jenna L. Pappalardo; David A. Hafler; David van Dijk (CHIL, 2020)

Self-supervised edge features for improved Graph Neural Network training. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (CoRR, 2020)

Permutation invariant networks to learn Wasserstein metrics. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (CoRR, 2020)

Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (AAAI, 2021)

Learning Potentials of Quantum Systems using Deep Neural Networks. - Arijit Sehanobish; Hector H. Corzo; Onur Kara; David van Dijk (AAAI Spring Symposium: MLPS, 2021)

Application of the Quantum Potential Neural Network to multi-electronic atoms. - Hector H. Corzo; Arijit Sehanobish; Onur Kara (CoRR, 2021)

Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models. - Onur Kara; Arijit Sehanobish; Hector H. Corzo (CoRR, 2021)

Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. - Arijit Sehanobish; Kawshik Kannan; Nabila Abraham; Anasuya Das; Benjamin Odry (EMNLP (Industry Track), 2022)

Hybrid Random Features. - Krzysztof Marcin Choromanski; Han Lin; Haoxian Chen 002; Arijit Sehanobish; Yuanzhe Ma; Deepali Jain; Jake Varley; Andy Zeng 001; Michael S. Ryoo; Valerii Likhosherstov; Dmitry Kalashnikov; Vikas Sindhwani; et al. (ICLR, 2022)

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. - Krzysztof Choromanski; Han Lin; Haoxian Chen 002; Tianyi Zhang; Arijit Sehanobish; Valerii Likhosherstov; Jack Parker-Holder; Tamás Sarlós; Adrian Weller; Thomas Weingarten (ICML, 2022)

Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports. - Arijit Sehanobish; McCullen Sandora; Nabila Abraham; Jayashri Pawar; Danielle Torres; Anasuya Das; Murray Becker; Richard Herzog; Benjamin Odry; Ron Vianu (NAACL-HLT (Industry Papers), 2022)

Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning. - Arijit Sehanobish; Nathaniel Brown; Ishita Daga; Jayashri Pawar; Danielle Torres; Anasuya Das; Murray Becker; Richard Herzog; Benjamin Odry; Ron Vianu (CoRR, 2022)

Efficient Graph Field Integrators Meet Point Clouds. - Krzysztof Marcin Choromanski; Arijit Sehanobish; Han Lin; Yunfan Zhao; Eli Berger; Tetiana Parshakova; Alvin Pan; David Watkins; Tianyi Zhang; Valerii Likhosherstov; Somnath Basu Roy Chowdhury; Kumar Avinava Dubey; et al. (ICML, 2023)

Scalable Neural Network Kernels. - Arijit Sehanobish; Krzysztof Marcin Choromanski; Yunfan Zhao; Kumar Avinava Dubey; Valerii Likhosherstov (ICLR, 2024)

Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. - Krzysztof Marcin Choromanski; Arijit Sehanobish; Somnath Basu Roy Chowdhury; Han Lin; Kumar Avinava Dubey; Tamás Sarlós; Snigdha Chaturvedi (NeurIPS, 2024)

Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning. - Arijit Sehanobish; Kumar Avinava Dubey; Krzysztof Marcin Choromanski; Somnath Basu Roy Chowdhury; Deepali Jain; Vikas Sindhwani; Snigdha Chaturvedi (NeurIPS, 2024)

Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning. - Arijit Sehanobish; Avinava Dubey; Krzysztof Choromanski; Somnath Basu Roy Chowdhury; Deepali Jain; Vikas Sindhwani; Snigdha Chaturvedi (CoRR, 2024)

Magnituder Layers for Implicit Neural Representations in 3D. - Sang Min Kim; Byeongchan Kim; Arijit Sehanobish; Krzysztof Choromanski; Dongseok Shim; Avinava Dubey; Min-hwan Oh (CoRR, 2024)

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs. - Krzysztof Marcin Choromanski; Isaac Reid; Arijit Sehanobish; Kumar Avinava Dubey (AISTATS, 2025)

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. - Somnath Basu Roy Chowdhury; Krzysztof Marcin Choromanski; Arijit Sehanobish; Kumar Avinava Dubey; Snigdha Chaturvedi (ICLR, 2025)