Stefano Martiniani

According to our database, Stefano Martiniani authored at least 7 papers between 2017 and 2025.

Collaborative distances:

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

2025

A unifying approach to self-organizing systems interacting via conservation laws.

Frank Barrows, Guanming Zhang, Satyam Anand, Zixi Chen, Jonathan Lin, Aman Desai, Stefano Martiniani, Francesco Caravelli

CoRR, July, 2025

Contrastive Self-Supervised Learning As Neural Manifold Packing.

Guanming Zhang, David J. Heeger, Stefano Martiniani

CoRR, June, 2025

A practical guide to machine learning interatomic potentials - Status and future.

Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood

CoRR, March, 2025

Open Materials Generation with Stochastic Interpolants.

Philipp Höllmer, Thomas Egg, Maya M. Martirossyan, Eric Fuemmeler, Amit Gupta, Zeren Shui, Pawan Prakash, Adrian Roitberg, Mingjie Liu, George Karypis, Mark K. Transtrum, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani

CoRR, February, 2025

2024

On the design space between molecular mechanics and machine learning force fields.

Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z. H. Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang, Daniel J. Cole, Joshua A. Rackers, Kyunghyun Cho, Joe G. Greener, Peter K. Eastman, Stefano Martiniani, Mark E. Tuckerman

CoRR, 2024

Unconditional stability of a recurrent neural circuit implementing divisive normalization.

Shivang Rawat, David J. Heeger, Stefano Martiniani

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

2017

Perspective: Energy Landscapes for Machine Learning.

Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales

CoRR, 2017