Publications associées à Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization

Publications associées (18)

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

EPFL2025

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Engineering novel protein interactions with therapeutic potential using deep learning-guided surface design

Anthony Marchand

Proteins are foundational biomolecules of life playing a crucial role in a myriad of biological processes. Their function often requires interplay with other biomolecules, including proteins themselves. Protein-protein interactions (PPIs) are essential for ...

EPFL2024

Distribution Testing with a Confused Collector

Nathaniel Idein Harms

We are interested in testing properties of distributions with systematically mislabeled samples. Our goal is to make decisions about unknown probability distributions, using a sample that has been collected by a confused collector, such as a machine-learni ...

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing2024

Robust Machine Learning - Distributed Methods for Safe AI

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may mis ...

Springer2024

Building typological classification in Switzerland using deep learning methods for seismic assessment

Annalisa Casciato, Pierino Lestuzzi, Alireza Khodaverdian

Natural disasters, such as earthquakes, have always represented a danger to human life. Seismic risk assessment consists of the evaluation of existing buildings and their expected response in case of an earthquake; the exposure model of buildings plays a k ...

Elsevier2023

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Nicolas Henri Bernard Flammarion, Scott William Pesme

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over diagonal linear networks. We prove the convergence of GD and SGD with macroscop ...

2023

Hyperdimensional computing for biosignal monitoring: Applications for epilepsy detection

Una Pale

Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...

EPFL2023

Charging and Electric Field Effects on Hydrogen Molecules Physisorbed on Graphene

Klaus Kern, Shai Mangel, Alessio Scavuzzo, Tobias Wollandt

Molecular hydrogen adsorbed on graphene was investigated by analyzing rotational excitation spectra obtained with a gate-tunable scanning tunneling microscope (STM). Through the shift of the rotational excitation energy, the tunability of physisorbed H2 on ...

AMER CHEMICAL SOC2023

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Sundar Subramaniam Ganesh

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

Helical versus linear Jahn-Teller distortions in allene and spiropentadiene radical cations

Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Marc Hamilton Folkmann Garner

The allene radical cation can be stabilized both by Jahn-Teller distortion of the bond lengths and by torsion of the end-groups. However, only the latter happens and the allene radical cation relaxes into a twisted D-2 symmetry structure with equal double- ...

ROYAL SOC CHEMISTRY2022

Optimal recovery of unsecured debt via interpretable reinforcement learning

Thomas Alois Weber, Michael Mark

This paper addresses the issue of interpretability and auditability of reinforcement-learning agents employed in the recovery of unsecured consumer debt. To this end, we develop a deterministic policy-gradient method that allows for a natural integration o ...

2022

Improving the Training of Compact Neural Networks for Visual Recognition

Shuxuan Guo

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

EPFL2022

Self-Exciting Point Processes: Identification and Control

Michael Mark

This thesis addresses theoretical and practical aspects of identification and subsequent control of self-exciting point processes. The main contributions correspond to four separate scientific papers. In the first paper, we address the challenge of robust ...

EPFL2022

Dynamic Range and Complexity Optimization of Mixed-Signal Machine Learning Systems

Andreas Peter Burg, Catherine Dehollain

Audio processing had been in demand throughout the electronic era. Recent advances in neural networks increased the demand on audio processing for speech recognition applications. In this work, a rigorous study on the dynamic range and system complexity op ...

IEEE2021

Helical electronic transitions of spiroconjugated molecules

Anne-Clémence Corminboeuf, Marc Hamilton Folkmann Garner

The two pi-systems of allene can combine into helical molecular orbitals (MOs), yet the helicity is lost in the pi-pi* transitions due to excited state mixing. In spiroconjugated molecules the relative orientation of the two pi-systems is different and con ...

Royal Society of Chemistry2021

Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

Luc Sébastien Bondaz

Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...

AIP Publishing2021

Mass-Zero constrained dynamics and statistics for the shell model in magnetic field

Sara Bonella, Alessandro Coretti, David Daniel Girardier

In several domains of physics, including first principle simulations and classical models for polarizable systems, the minimization of an energy function with respect to a set of auxiliary variables must be performed to define the dynamics of physical degr ...

EDP Sciences, Springer2021