Arijit Sehanobish

38 papers A* 9A 1Journal 23Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SWING: Unlocking Implicit Graph Representations for Graph Random Features. CoRR Alessandro Manenti, Avinava Dubey, Arijit Sehanobish, Cesare Alippi, Krzysztof Choromanski
2025	J	`jnl`	Computationally-efficient Graph Modeling with Refined Graph Random Features. CoRR Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish, Isaac Reid
2025	J	`jnl`	Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings. CoRR Neal G. Ravindra, Arijit Sehanobish
2025	A	`conf`	Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs. AISTATS Krzysztof Marcin Choromanski, Isaac Reid, Arijit Sehanobish, Kumar Avinava Dubey
2025	A*	`conf`	Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. ICLR Somnath Basu Roy Chowdhury, Krzysztof Marcin Choromanski, Arijit Sehanobish, Kumar Avinava Dubey, Snigdha Chaturvedi
2025	J	`jnl`	Wavelet-Induced Rotary Encodings: RoPE Meets Graphs. CoRR Isaac Reid, Arijit Sehanobish, Cederik Höfs, Bruno Mlodozeniec, Leonhard Vulpius, Federico Barbero, Adrian Weller, Krzysztof Choromanski, Richard E. Turner, Petar Velickovic
2025	—	`conf`	When Context Grows, So Does the Challenge: Human Oversight in LLM Evaluation of Financial Tables. UIST Adjunct Arijit Sehanobish, Shirley Anderson, Guillaume Michel, Mike Arov
2024	A*	`conf`	Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. NeurIPS Krzysztof Marcin Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Han Lin, Kumar Avinava Dubey, Tamás Sarlós, Snigdha Chaturvedi
2024	J	`jnl`	Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. CoRR Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Han Lin, Avinava Dubey, Tamás Sarlós, Snigdha Chaturvedi
2024	J	`jnl`	Magnituder Layers for Implicit Neural Representations in 3D. CoRR Sang Min Kim, Byeongchan Kim, Arijit Sehanobish, Krzysztof Choromanski, Dongseok Shim, Avinava Dubey, Min-hwan Oh
2024	J	`jnl`	Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs. CoRR Krzysztof Choromanski, Isaac Reid, Arijit Sehanobish, Avinava Dubey
2024	A*	`conf`	Scalable Neural Network Kernels. ICLR Arijit Sehanobish, Krzysztof Marcin Choromanski, Yunfan Zhao, Kumar Avinava Dubey, Valerii Likhosherstov
2024	J	`jnl`	Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning. CoRR Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi
2024	A*	`conf`	Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning. NeurIPS Arijit Sehanobish, Kumar Avinava Dubey, Krzysztof Marcin Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi
2024	J	`jnl`	Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning. CoRR Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish, Avinava Dubey, Snigdha Chaturvedi
2023	A*	`conf`	Efficient Graph Field Integrators Meet Point Clouds. ICML 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
2023	J	`jnl`	Efficient Graph Field Integrators Meet Point Clouds. CoRR 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
2023	J	`jnl`	Scalable Neural Network Kernels. CoRR Arijit Sehanobish, Krzysztof Choromanski, Yunfan Zhao, Avinava Dubey, Valerii Likhosherstov
2022	J	`jnl`	Efficient Extraction of Pathologies from C-Spine Radiology Reports using Multi-Task Learning. CoRR Arijit Sehanobish, Nathaniel Brown, Ishita Daga, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
2022	—	`conf`	Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports. NAACL-HLT (Industry Papers) Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
2022	J	`jnl`	Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports. CoRR Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
2022	A*	`conf`	From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. ICML Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten
2022	A*	`conf`	Hybrid Random Features. ICLR 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
2022	—	`conf`	Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. EMNLP (Industry Track) Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry
2022	J	`jnl`	Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks. CoRR Arijit Sehanobish, Kawshik Kannan, Nabila Abraham, Anasuya Das, Benjamin Odry
2021	J	`jnl`	Application of the Quantum Potential Neural Network to multi-electronic atoms. CoRR Hector H. Corzo, Arijit Sehanobish, Onur Kara
2021	J	`jnl`	Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models. CoRR Onur Kara, Arijit Sehanobish, Hector H. Corzo
2021	A*	`conf`	Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. AAAI Arijit Sehanobish, Neal G. Ravindra, David van Dijk
2021	J	`jnl`	Hybrid Random Features. CoRR 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
2021	—	`conf`	Learning Potentials of Quantum Systems using Deep Neural Networks. AAAI Spring Symposium: MLPS Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk
2020	J	`jnl`	Disease State Prediction From Single-Cell Data Using Graph Attention Networks. CoRR Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk
2020	—	`conf`	Disease state prediction from single-cell data using graph attention networks. CHIL Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk
2020	J	`jnl`	Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks. CoRR Arijit Sehanobish, Neal G. Ravindra, David van Dijk
2020	J	`jnl`	Learning Potentials of Quantum Systems using Deep Neural Networks. CoRR Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk
2020	J	`jnl`	Permutation invariant networks to learn Wasserstein metrics. CoRR Arijit Sehanobish, Neal G. Ravindra, David van Dijk
2020	J	`jnl`	Self-supervised edge features for improved Graph Neural Network training. CoRR Arijit Sehanobish, Neal G. Ravindra, David van Dijk
2020	A*	`conf`	Using Chinese Glyphs for Named Entity Recognition (Student Abstract). AAAI Chan Hee Song, Arijit Sehanobish
2019	J	`jnl`	Using Chinese Glyphs for Named Entity Recognition. CoRR Arijit Sehanobish, Chan Hee Song