most citedOn the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.RO2026

Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors

Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy +1

Tactile sensing is an essential modality for robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trained image encoders are stand…

physics.comp-ph2024★ 1 cited

On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants

Fedor S. Avilov, Roman A. Eremin, Semen A. Budennyy +1

Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliabilit…