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Arijit Sehanobish
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2020 – today
- 2024
- [c10]Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Kumar Avinava Dubey, Valerii Likhosherstov:
Scalable Neural Network Kernels. ICLR 2024 - [i17]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 abs/2406.15881 (2024) - [i16]Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish, Avinava Dubey, Snigdha Chaturvedi:
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. CoRR abs/2406.16257 (2024) - [i15]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 abs/2406.17740 (2024) - 2023
- [c9]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: 5978-6004 - [i14]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 abs/2302.00942 (2023) - [i13]Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov:
Scalable Neural Network Kernels. CoRR abs/2310.13225 (2023) - 2022
- [c8]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: 332-347 - [c7]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 - [c6]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: 3962-3983 - [c5]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: 130-140 - [i12]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 abs/2204.04544 (2022) - [i11]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 abs/2205.02979 (2022) - [i10]Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry:
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. CoRR abs/2210.13979 (2022) - 2021
- [c4]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: 4864-4873 - [c3]Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk:
Learning Potentials of Quantum Systems using Deep Neural Networks. AAAI Spring Symposium: MLPS 2021 - [i9]Hector H. Corzo, Arijit Sehanobish, Onur Kara
:
Application of the Quantum Potential Neural Network to multi-electronic atoms. CoRR abs/2106.08138 (2021) - [i8]Onur Kara
Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models. CoRR abs/2109.13925 (2021) - [i7]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 abs/2110.04367 (2021) - 2020
- [c2]Chan Hee Song, Arijit Sehanobish:
Using Chinese Glyphs for Named Entity Recognition (Student Abstract). AAAI 2020: 13921-13922 - [c1]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: 121-130 - [i6]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 abs/2002.07128 (2020) - [i5]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 abs/2006.12971 (2020) - [i4]Arijit Sehanobish, Hector H. Corzo, Onur Kara
Learning Potentials of Quantum Systems using Deep Neural Networks. CoRR abs/2006.13297 (2020) - [i3]Arijit Sehanobish, Neal G. Ravindra, David van Dijk:
Self-supervised edge features for improved Graph Neural Network training. CoRR abs/2007.04777 (2020) - [i2]Arijit Sehanobish, Neal G. Ravindra, David van Dijk:
Permutation invariant networks to learn Wasserstein metrics. CoRR abs/2010.05820 (2020)
2010 – 2019
- 2019
- [i1]Arijit Sehanobish, Chan Hee Song:
Using Chinese Glyphs for Named Entity Recognition. CoRR abs/1909.09922 (2019)
Coauthor Index
aka: Krzysztof Marcin Choromanski
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