Learning new physics from an imperfect machine

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Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

High-level waste, stemming from nuclear electricity generation poses significant environmental and safety concerns. Currently, high-level wastes are stored in interim facilities needing constant monitoring and waiting for a definitive solution. Deep geolog ...

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood F ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

In the standard model of particle physics, the chiral anomaly can occur in relativistic plasmas and plays a role in the early Universe, protoneutron stars, heavy-ion collisions, and quantum materials. It gives rise to a magnetic instability if the number d ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the se ...

Learning new physics from an imperfect machine

Chattez avec Graph Search

Robust machine learning for neuroscientific inference

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Social Opinion Formation and Decision Making Under Communication Trends

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Random matrix methods for high-dimensional machine learning models

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Enabling Uncertainty Estimation in Iterative Neural Networks

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

Toward Reliable Human Pose Forecasting With Uncertainty

Aggregating Spatial and Photometric Context for Photometric Stereo

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Chiral Anomaly and Dynamos from Inhomogeneous Chemical Potential Fluctuations

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data