Publications related to Automated machine learning

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Sébastien Marcel, Alexander Carl Unnervik

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how steal ...

IEEE2022

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Sébastien Marcel, Alexander Carl Unnervik

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how steal ...

IEEE2022

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Kevin Rossi

Li-S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulfide (LiPS) shuttling. Computational screening of L ...

WILEY2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

High-strength metal alloys achieve their performance via careful control of precipitates and solutes.The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...

EPFL2022

Trace norm regularization for multi-task learning with scarce data

Nicolas Henri Bernard Flammarion, Etienne Patrice Boursier, Mikhail Konobeev

Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples. Motivated by the recent success of neural networks applied to data-scarce tasks, we consider a linear low-dimensional shared representation mode ...

2022

Meta Transfer Learning for Early Success Prediction in MOOCs

Vinitra Swamy, Mirko Marras

Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a cou ...

ACM2022

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Guillermo Ortiz Jimenez, Gizem Yüce, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

A Solution to Adaptive Mobile Manipulator Throwing

Aude Billard, Yang Liu, Aradhana Nayak

Mobile manipulator throwing is a promising method to increase the flexibility and efficiency of dynamic manipulation in factories. Its major challenge is to efficiently plan a feasible throw under a wide set of task specifications. We show that the mobile ...

IEEE2022

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An anomaly detection approach for backdoored neural networks: face recognition as a case study

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Beyond potentials: Integrated machine learning models for materials

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Neural Network Potentials for Age Hardening Aluminum Alloys

Trace norm regularization for multi-task learning with scarce data

Meta Transfer Learning for Early Success Prediction in MOOCs

A Structured Dictionary Perspective on Implicit Neural Representations

A Solution to Adaptive Mobile Manipulator Throwing

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Meta Transfer Learning for Early Success Prediction in MOOCs

Beyond potentials: Integrated machine learning models for materials

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Neural Network Potentials for Age Hardening Aluminum Alloys

A Structured Dictionary Perspective on Implicit Neural Representations

Trace norm regularization for multi-task learning with scarce data

A Solution to Adaptive Mobile Manipulator Throwing