Concept
Natural exponential family
Related publications (32)
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons
Julien René Pierre Fageot, Sadegh Farhadkhani, Oscar Jean Olivier Villemaud, Le Nguyen Hoang
Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model and its variants have been widely used to do so. The historical model conside ...
AAAI Press2024Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Changpeng Lin, Hong Zhang, Chen Shen, Yong Zhao
Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...
NATURE PORTFOLIO2023Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise
Michael Christoph Gastpar, Alper Köse, Ahmet Arda Atalik
The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the co ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes
We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...
EPFL2022Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering
In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pro ...
EPFL2022Learning in Volatile Environments With the Bayes Factor Surprise
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Vasiliki Liakoni
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...
MIT PRESS2021