Arijit Sehanobish

According to our database, Arijit Sehanobish authored at least 21 papers between 2019 and 2024.

Collaborative distances:

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

2024

Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs.

Krzysztof Choromanski, Isaac Reid, Arijit Sehanobish, Avinava Dubey

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

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

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning.

Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish, Avinava Dubey, Snigdha Chaturvedi

CoRR, 2024

Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers.

Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Han Lin, Avinava Dubey, Tamás Sarlós, Snigdha Chaturvedi

CoRR, 2024

Scalable Neural Network Kernels.

Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Kumar Avinava Dubey, Valerii Likhosherstov

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

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, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller

Proceedings of the International Conference on Machine Learning, 2023

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

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

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, 2022

From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers.

Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten

Proceedings of the International Conference on Machine Learning, 2022

Hybrid Random Features.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks.

Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: EMNLP 2022 - Industry Track, Abu Dhabi, UAE, December 7, 2022

2021

Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models.

Onur Kara, Arijit Sehanobish, Hector H. Corzo

CoRR, 2021

Application of the Quantum Potential Neural Network to multi-electronic atoms.

Hector H. Corzo, Arijit Sehanobish, Onur Kara

CoRR, 2021

Learning Potentials of Quantum Systems using Deep Neural Networks.

Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Permutation invariant networks to learn Wasserstein metrics.

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

CoRR, 2020

Self-supervised edge features for improved Graph Neural Network training.

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

CoRR, 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

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

Using Chinese Glyphs for Named Entity Recognition (Student Abstract).

Chan Hee Song, Arijit Sehanobish

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Using Chinese Glyphs for Named Entity Recognition.

Arijit Sehanobish, Chan Hee Song

CoRR, 2019