Ali Parviz

According to our database, Ali Parviz authored at least 11 papers between 2022 and 2025.

Collaborative distances:

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

2025

Scalable and Cost-Efficient de Novo Template-Based Molecular Generation.

Piotr Gainski, Oussama Boussif, Andrei Rekesh, Dmytro Shevchuk, Ali Parviz, Mike Tyers, Robert A. Batey, Michal Koziarski

CoRR, June, 2025

Are Large Language Models Good Temporal Graph Learners?

Shenyang Huang, Ali Parviz, Emma Kondrup, Zachary Yang, Zifeng Ding, Michael M. Bronstein, Reihaneh Rabbany, Guillaume Rabusseau

CoRR, June, 2025

2024

Best of Both Worlds: Advantages of Hybrid Graph Sequence Models.

Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni

CoRR, 2024

MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning.

Kerstin Kläser, Blazej Banaszewski, Samuel Maddrell-Mander, Callum McLean, Luis Müller, Ali Parviz, Shenyang Huang, Andrew W. Fitzgibbon

CoRR, 2024

Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory.

Alexander Mathiasen, Hatem Helal, Paul Balanca, Adam Krzywaniak, Ali Parviz, Frederik Hvilshøj, Blazej Banaszewski, Carlo Luschi, Andrew William Fitzgibbon

CoRR, 2024

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs.

Julia Gastinger, Shenyang Huang, Michael Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets.

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michal Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew W. Fitzgibbon, Blazej Banaszewski, Chad Martin, Dominic Masters

CoRR, 2023

Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning.

Ehsan Beikihassan, Amy K. Hoover, Ioannis Koutis, Ali Parviz, Niloofar Aghaieabiane

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

2022

Generalized Laplacian Positional Encoding for Graph Representation Learning.

Sohir Maskey, Ali Parviz, Maximilian Thiessen, Hannes Stärk, Ylli Sadikaj, Haggai Maron

CoRR, 2022

Long Range Graph Benchmark.

Vijay Prakash Dwivedi, Ladislav Rampásek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022