10 papers
Contrastive learning of dynamical representations for enhanced molecular sampling
Kai Zhu, Jintu Zhang, Pietro Novelli +2
Identifying collective variables that capture slow dynamical modes is essential for sampling rare events in complex systems. Existing machine-learning approaches often require pred…
Reward-free Pretraining for Reinforcement Learning via Occupancy Coverage Maximization
Marco Pratticò, Pietro Novelli, Massimiliano Pontil +1
Sparse rewards pose a central challenge in reinforcement learning, since agents receive no informative signal until they reach their goal. Intrinsic-reward methods address this iss…
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
Giacomo Turri, Grégoire Pacreau, Giacomo Meanti +8
kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model b…
SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC
Dmitriy Poteryayev, Pietro Novelli, Annalisa Coriolano +7
Raman spectroscopy is a key tool for graphene characterization, yet its application to graphene grown on silicon carbide (SiC) is strongly limited by the intense and variable secon…
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decompos…
Neural Conditional Probability for Uncertainty Quantification
Vladimir R. Kostic, Karim Lounici, Gregoire Pacreau +3
We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with a focus on statistical inference tasks. NCP can be used…