Arijit Sehanobish

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 27

Top Topics

State Variables

Mahalanobis Metric

Top Venues

Publications

Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish, Avinava Dubey, Snigdha Chaturvedi
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. CoRR (2024)
Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Kumar Avinava Dubey, Valerii Likhosherstov
Scalable Neural Network Kernels. ICLR (2024)
Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning. CoRR (2024)
Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Han Lin, Avinava Dubey, Tamás Sarlós, Snigdha Chaturvedi
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. CoRR (2024)
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, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller
Efficient Graph Field Integrators Meet Point Clouds. ICML (2023)
Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov
Scalable Neural Network Kernels. CoRR (2023)
Krzysztof Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Avinava Dubey, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller
Efficient Graph Field Integrators Meet Point Clouds. CoRR (2023)
Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. CoRR (2022)
Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. ICML (2022)
Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports. CoRR (2022)
Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports. NAACL-HLT (Industry Papers) (2022)
Arijit Sehanobish, Nathaniel Brown, Ishita Daga, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning. CoRR (2022)
Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. EMNLP (Industry Track) (2022)
Krzysztof Marcin Choromanski, Han Lin, Haoxian Chen, Arijit Sehanobish, Yuanzhe Ma, Deepali Jain, Jake Varley, Andy Zeng, Michael S. Ryoo, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
Hybrid Random Features. ICLR (2022)
Onur Kara, Arijit Sehanobish, Hector H. Corzo
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models. CoRR (2021)
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. AAAI (2021)
Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S. Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller
Hybrid Random Features. CoRR (2021)
Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk
Learning Potentials of Quantum Systems using Deep Neural Networks. AAAI Spring Symposium: MLPS (2021)
Hector H. Corzo, Arijit Sehanobish, Onur Kara
Application of the Quantum Potential Neural Network to multi-electronic atoms. CoRR (2021)
Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk
Disease State Prediction From Single-Cell Data Using Graph Attention Networks. CoRR (2020)
Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk
Disease state prediction from single-cell data using graph attention networks. CHIL (2020)
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Permutation invariant networks to learn Wasserstein metrics. CoRR (2020)
Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk
Learning Potentials of Quantum Systems using Deep Neural Networks. CoRR (2020)
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks. CoRR (2020)
Chan Hee Song, Arijit Sehanobish
Using Chinese Glyphs for Named Entity Recognition (Student Abstract). AAAI (2020)
Arijit Sehanobish, Neal G. Ravindra, David van Dijk
Self-supervised edge features for improved Graph Neural Network training. CoRR (2020)
Arijit Sehanobish, Chan Hee Song
Using Chinese Glyphs for Named Entity Recognition. CoRR (2019)