Publications associées à Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Publications associées (21)

High-dimensional learning of narrow neural networks

Recent years have been marked with the fast-pace diversification and increasing ubiquity of machine learning (ML) applications. Yet, a firm theoretical understanding of the surprising efficiency of neural networks (NNs) to learn from high-dimensional data ...

2025

OPTIMAL TRANSPORT-INSPIRED DEEP LEARNING FRAMEWORK FOR SLOW-DECAYING KOLMOGOROV \bfitn-WIDTH PROBLEMS: EXPLOITING SINKHORN LOSS AND WASSERSTEIN KERNEL

Federico Pichi

Reduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the und ...

2025

Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI

Meritxell Bach Cuadra

To date, several methods have been developed to explain deep learning algorithms for classification tasks. Recently, an adaptation of two of such methods has been proposed to generate instance-level explainable maps in a semantic segmentation scenario, suc ...

Springer2025

Predicting electronic screening for fast Koopmans spectral functional calculations

Nicola Marzari

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of elect ...

2024

Investigation of palladium(IV) species in organic synthesis and synthetic studies towards meloyunnanine A

Takuji Fujii

This dissertation addresses two research themes in modern organic synthesis. The first topic explores the development of new reactions enabled by Pd(IV)-involved catalytic cycles. The second theme investigates the synthetic studies towards total synthesis ...

EPFL2024

Wigner kernels: Body-ordered equivariant machine learning without a basis

Michele Ceriotti, Sergey Pozdnyakov, Filippo Bigi

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

AIP Publishing2024

Efficient co-upcycling of glycerol and CO2 into valuable products enabled by a bifunctional Ru-complex catalyst

Jiaqi Xu

Glycerol and CO2 are largely produced as by-products in modern industry. Herein, three Ru complexes bearing air- and water-stable NHC-nitrogen-nitrogen ligands are designed as bifunctional catalysts to upcycle glycerol and CO2 simultaneously. Among them, R ...

Royal Society of Chemistry2024

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Devis Tuia, Antoine Bosselut, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol, Li Mi

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

Springer Cham2024

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Michaël Unser, Dimitris Perdios, Alexis Marie Frederic Goujon, Pakshal Narendra Bohra, Sebastian Jonas Neumayer, Stanislas Ducotterd

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empi ...

2024

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Kevin Rossi

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks

Xianlin Wang

Integrating various reinforcements into 3D concrete printing (3DCP) is an efficient method to satisfy critical requirements for structural applications. This paper explores an explainable ensemble machine learning (EML) method to predict the bond failure m ...

Elsevier2023

Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

Devis Tuia, Marc Conrad Russwurm, Lloyd Haydn Hughes, Jan Pisl

Tropical deforestation is a major environmental problem with severe consequences such as carbon emissions or biodiversity loss. While much research focuses on monitoring and mapping deforestation, less attention is paid to understanding the various reasons ...

Institute of Electrical and Electronics Engineers2023

Metasurface-Enhanced Infrared Spectroscopy: An Abundance of Materials and Functionalities

Hatice Altug, Andreas Tittl, Aurélian Michel John-Herpin, Felix Ulrich Brikh

Infrared spectroscopy provides unique information on the composition and dynamics of biochemical systems by resolving the characteristic absorption fingerprints of their constituent molecules. Based on this inherent chemical specificity and the capability ...

Wiley-V C H Verlag Gmbh2023

Inferring how animals deform improves cell tracking

Sahand Jamal Rahi

Tracking cells is a time-consuming part of biological image analysis, and traditional manual annotation methods are prohibitively laborious for tracking neurons in the deforming and moving Caenorhabditis elegans brain. By leveraging machine learning to dev ...

Nature Publishing Group2023

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

Tao Lin

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

ASSOC COMPUTING MACHINERY2022

A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

Nikolaos Geroliminis, Claudia Bongiovanni

This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

IEEE2022

Mapping Catalyst‐Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions

Anne-Clémence Corminboeuf, Nicolai Cramer, Matthew Wodrich, Simone Gallarati, Lukasz Piotr Wozniak, Miyeon Chang

Group 9 metals, in particular Rh III complexes with cyclopentadienyl ligands, are competent C–H activation catalysts. Recently, a Cp*Rh III catalyzed reaction of alkenes with N -enoxyphthalimides showed divergent outcome based on the solvent, with carboami ...

2022

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Xingchen Yang

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022