Publications related to JAX-COSMO: AN END-TO-END DIFFERENTIABLE AND GPU ACCELERATED COSMOLOGY LIBRARY

Related publications (12)

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

EPFL2025

SAMPa: Sharpness-aware Minimization Parallelized

Volkan Cevher, Thomas Michaelsen Pethick, Wanyun Xie

Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires sequentially computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We ...

2024

Optimal Inference in Contextual Stochastic Block Models

Lenka Zdeborová, Odilon Marie Cyprien Duranthon

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Journal of Machine Learning Research Inc.2024

BAYESIAN MODELING OF INSURANCE CLAIMS FOR HAIL DAMAGE

Anthony Christopher Davison

Despite its importance for insurance, there is almost no literature on statistical hail damage modeling. Statistical models for hailstorms exist, though they are generally not open-source, but no study appears to have developed a stochastic hail impact fun ...

2024

Backpropagation-free Training of Analog AI Accelerators

Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Institute of Electrical and Electronics Engineers Inc.2024

Plasma profile reconstruction supported by kinetic modeling

Pedro Andres Molina Cabrera

Combining the analysis of multiple diagnostics and well-chosen prior information in the framework of Bayesian probability theory, the Integrated Data Analysis code (IDA Fischer et al 2010 Fusion Sci. Technol. 58 675-84) can provide density and temperature ...

IOP Publishing Ltd2024

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Yoeri Renier Johannes Poels

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles ...

2024

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib

Roberto Castello, Federico Amato

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is st ...

2024

DECENTRALIZED FUSION OF EXPERTS OVER NETWORKS

Ali H. Sayed

This article considers a network of agents interested in solving a classification t ask. The datasets available to accomplish the task are heterogeneous and dispersed across the agents. Each agent is inter-ested in discriminating among the 'inner' hypothes ...

IEEE Computer Society2024

Statistics for Covariance Operators: Transport, Flows, and Diffusions

Leonardo Vincenzo Santoro

This thesis is concerned with covariance operators in statistics, both as object of statistical inference (Chapters 2,3 ) and as tool for statistical inference (Chapter 4). The first part of this thesis considers the non-linear and infinite-dimensional ...

EPFL2024

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Sandro Carrara, Francesca Rodino, Lin Du

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

Pergamon-Elsevier Science Ltd2024

EOS: a software for flavor physics phenomenology

Elena Graverini

EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle physics, Bayesian inference of theory parameters from experimental and theoreti ...

2022