Chaitanya K. Joshi

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 27

Top Topics

Sketch Recognition

Protein Structure

Travelling Salesman

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Nat. Mac. Intell.

Publications

Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya K. Joshi, Pietro Liò, Shantanu Singh, Andreas Bender, Pietro Sormanni, Matthew Greenig
Understanding Biology in the Age of Artificial Intelligence. CoRR (2024)
Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan Sheng Foo
On Representation Knowledge Distillation for Graph Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35 (4) (2024)
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. CoRR (2024)
Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell
Evaluating representation learning on the protein structure universe. CoRR (2024)
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
Evaluating Representation Learning on the Protein Structure Universe. ICLR (2024)
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson
Benchmarking Graph Neural Networks. J. Mach. Learn. Res. 24 (2023)
Kamil Bujel, Yonatan Gideoni, Chaitanya K. Joshi, Pietro Liò
Group Invariant Global Pooling. CoRR (2023)
Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò
Multi-State RNA Design with Geometric Multi-Graph Neural Networks. CoRR (2023)
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 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
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR (2023)
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR (2023)
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
The Second Learning on Graphs Conference: Preface. LoG (2023)
Ramón Viñas, Chaitanya K. Joshi, Dobrik Georgiev, Phillip Lin, Bianca Dumitrascu, Eric R. Gamazon, Pietro Liò
Hypergraph factorization for multi-tissue gene expression imputation. Nat. Mac. Intell. 5 (7) (2023)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio
On the Expressive Power of Geometric Graph Neural Networks. ICML (2023)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò
On the Expressive Power of Geometric Graph Neural Networks. CoRR (2023)
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent
Learning the travelling salesperson problem requires rethinking generalization. Constraints An Int. J. 27 (1-2) (2022)
Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis. 3DV (2022)
Peng Xu, Chaitanya K. Joshi, Xavier Bresson
Multigraph Transformer for Free-Hand Sketch Recognition. IEEE Trans. Neural Networks Learn. Syst. 33 (10) (2022)
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
The First Learning on Graphs Conference: Preface. LoG (2022)
Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo
On Representation Knowledge Distillation for Graph Neural Networks. CoRR (2021)
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent
Learning TSP Requires Rethinking Generalization. CP (2021)
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, Xavier Bresson
Benchmarking Graph Neural Networks. CoRR (2020)
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent, Xavier Bresson
Learning TSP Requires Rethinking Generalization. CoRR (2020)
Peng Xu, Chaitanya K. Joshi, Xavier Bresson
Multi-Graph Transformer for Free-Hand Sketch Recognition. CoRR (2019)
Kuhu Joshi, Chaitanya K. Joshi
Working women and caste in India: A study of social disadvantage using feature attribution. CoRR (2019)
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
On Learning Paradigms for the Travelling Salesman Problem. CoRR (2019)
Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem. CoRR (2019)
Chaitanya K. Joshi, Fei Mi, Boi Faltings
Personalization in Goal-Oriented Dialog. CoRR (2017)