Chaitanya K. Joshi

According to our database, Chaitanya K. Joshi authored at least 25 papers between 2017 and 2024.

Collaborative distances:

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

2024

On Representation Knowledge Distillation for Graph Neural Networks.

Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan Sheng Foo

IEEE Trans. Neural Networks Learn. Syst., April, 2024

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design.

Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò

CoRR, 2024

Understanding Biology in the Age of Artificial Intelligence.

Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya K. Joshi, Pietro Liò, Shantanu Singh, Andreas Bender, Pietro Sormanni, Matthew Greenig

CoRR, 2024

Evaluating Representation Learning on the Protein Structure Universe.

Arian Rokkum Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Hypergraph factorization for multi-tissue gene expression imputation.

Ramón Viñas, Chaitanya K. Joshi, Dobrik Georgiev, Phillip Lin, Bianca Dumitrascu, Eric R. Gamazon, Pietro Liò

Nat. Mac. Intell., July, 2023

Hypergraph Factorisation Expression Quantitative Trait Loci.

Ramón Viñas, Chaitanya K. Joshi, Dobrik Georgiev, Phillip Lin, Bianca Dumitrascu, Pietro Liò, Eric R. Gamazon

Dataset, May, 2023

Benchmarking Graph Neural Networks.

Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

J. Mach. Learn. Res., 2023

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein

CoRR, 2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

Group Invariant Global Pooling.

Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Liò

CoRR, 2023

Multi-State RNA Design with Geometric Multi-Graph Neural Networks.

Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò

CoRR, 2023

The Second Learning on Graphs Conference: Preface.

Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

On the Expressive Power of Geometric Graph Neural Networks.

Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio

Proceedings of the International Conference on Machine Learning, 2023

2022

Multigraph Transformer for Free-Hand Sketch Recognition.

Peng Xu, Chaitanya K. Joshi, Xavier Bresson

IEEE Trans. Neural Networks Learn. Syst., 2022

Learning the travelling salesperson problem requires rethinking generalization.

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

Constraints An Int. J., 2022

The First Learning on Graphs Conference: Preface.

Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov

Proceedings of the Learning on Graphs Conference, 2022

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis.

Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin

Proceedings of the International Conference on 3D Vision, 2022

2021

Learning TSP Requires Rethinking Generalization.

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

Proceedings of the 27th International Conference on Principles and Practice of Constraint Programming, 2021

2020

Learning TSP Requires Rethinking Generalization.

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent, Xavier Bresson

CoRR, 2020

Benchmarking Graph Neural Networks.

Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, Xavier Bresson

CoRR, 2020

2019

Multi-Graph Transformer for Free-Hand Sketch Recognition.

Peng Xu, Chaitanya K. Joshi, Xavier Bresson

CoRR, 2019

On Learning Paradigms for the Travelling Salesman Problem.

Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

CoRR, 2019

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem.

Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

CoRR, 2019

Working women and caste in India: A study of social disadvantage using feature attribution.

Kuhu Joshi, Chaitanya K. Joshi

CoRR, 2019

2017

Personalization in Goal-Oriented Dialog.

Chaitanya K. Joshi, Fei Mi, Boi Faltings

CoRR, 2017