Publication
Deep Learning for Logic Optimization Algorithms
Publications associées (92)
Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...
Amer Chemical Soc2024Scalable constrained optimization
Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...
EPFL2024Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning
Berend Smit, Xiaoqi Zhang, Sauradeep Majumdar, Hyunsoo Park
The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...
Royal Soc Chemistry2024A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering
François Maréchal, Julia Granacher
In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...
Oxford2023Augmented Lagrangian Methods for Provable and Scalable Machine Learning
Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
EPFL2023Learning-based techniques for lensless reconstruction
In this internship, I explore different optimization algorithms for lensless imaging. Lensless imaging is a new imaging technique that replaces the lens of a camera with a diffuser mask. This allows for simpler and cheaper camera hardware. However, the rec ...
2023C-shells: Deployable Gridshells with Curved Beams
Mark Pauly, Francis Julian Panetta, Seiichi Eduardo Suzuki Erazo, Quentin Christian Becker, Yingying Ren, Davide Pellis
We introduce a computational pipeline for simulating and designing C-shells, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-fre ...
2023Bayesian Optimization for Chemical Reactions
Philippe Schwaller, Jeff Guo, Bojana Rankovic
Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...
Bern2023Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
Rachid Guerraoui, Andrei Kucharavy, Matteo Monti
Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...
New York2023A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission
Jennifer Maria Frieden, Chiara Perrina, Xin Wu, Arshia Ruina, Andrii Kotenko
A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant perfor ...
ELSEVIER2023