Concept

Automated machine learning

Publications associées (263)

Predicting in Uncertain Environments: Methods for Robust Machine Learning

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Measurement in the Age of Information

Marilyne Andersen, Forrest Simon Webler

Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from ...

2022

Hands-on reservoir computing: a tutorial for practical implementation

Matteo Cucchi, Giuseppe Ciccone

This manuscript serves a specific purpose: to give readers from fields such as material science, chemistry, or electronics an overview of implementing a reservoir computing (RC) experiment with her/his material system. Introductory literature on the topic ...

IOP Publishing Ltd2022

Machine Learning for Musculoskeletal Modeling of Upper Extremity

Rahul Sharma

We propose a novel machine learning (ML)-driven methodology to estimate biomechanical variables of interest traditionally obtained from upper-extremity musculoskeletal (MSK) modeling. MSK models facilitate personalized modeling, perform "what-if" analyses, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Federico Grasselli, Chiheb Ben Mahmoud

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Xianlin Wang, Yuqing Liu

The shear resistance of headed studs is of paramount importance for the design of steel-concrete composite structures and an accurate predictive model is highly needed. Ensemble learning is expected to be a powerful solution while it relies on laborious se ...

2022

Contrastive Feature Learning for Railway Infrastructure Fault Diagnostic

Olga Fink

To operate the railway system safely and efficiently, a multitude of assets need to me monitored. Railway sleepers are one of these infrastructure assets, that are safety critical. To automate the monitoring process, data-driven fault diagnostics models ha ...

Research Publishing2022

Financial Risk Management with Machine Learning

Marc-Aurèle Antoine Divernois

This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...

EPFL2022

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere.The recipe for the frangipane cake is an algorithm.If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven.Now, what ...

EPFL2022

Dif-MAML: Decentralized Multi-Agent Meta-Learning

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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