5 papers
Learning from almost nothing: How neural networks survive heavy input corruption
Justin Tahmassebpur, Asadullah Bhuiyan, Hyejin Kim +1
Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnability. Here we examine attribut…
Graphlet Histogram Representation Database of Inorganic Crystals
Aaditya Panigrahi, Yanjun Liu, Omri Lesser +2
Machine learning models for materials property prediction increasingly rely on representations learned end-to-end from large density-functional-theory databases, limiting their app…
Competing nonlinearities, criticality, and order-to-chaos transition in deep networks
Omri Lesser, Debanjan Chowdhury
Deep neural networks owe their expressive power to nonlinear activation functions. The effective field theory of signal propagation at initialization reveals a few distinct univers…
Electron affinity difference distributions guide the discovery of the superconductor PtPbBi
Omri Lesser, Yanjun Liu, Natalie Maus +11
Predicting the superconducting transition temperature () from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence…
Melting point depression of charge density wave in 1T-TiSe due to size effects
Saif Siddique, Mehrdad T. Kiani, Omri Lesser +13
Classical nucleation theory predicts size-dependent nucleation and melting due to surface and confinement effects at the nanoscale. In correlated electronic states, observation of…