Publications related to Completeness of Atomic Structure Representations

Related publications (26)

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Pascal Fua

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Springer Science and Business Media Deutschland GmbH2025

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Yves Rychener

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

EPFL2025

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Yung-Cheng Chiang

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

2025

Predicting climate-change impacts on the global glacier-fed stream microbiome

Tom Ian Battin, Hannes Markus Peter, Tyler Joe Kohler, Grégoire Marie Octave Edouard Michoud, Massimo Bourquin, Matteo Tolosano, Vincent Henri De Staercke, Stylianos Fodelianakis, Andrew Lean Robison, Aileen Ute Geers

The shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 pr ...

NATURE PORTFOLIO2025

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Guillaume André Jean Fraux, Arslan Mazitov, Nataliya Lopanitsyna

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

Balancing computational chemistry's potential with its environmental impact

Philippe Schwaller, Oliver Tobias Schilter

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often ...

ROYAL SOC CHEMISTRY2024

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Yuchen Yang

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

Institute of Electrical and Electronics Engineers Inc.2024

Prediction rigidities for data-driven chemistry

Michele Ceriotti, Federico Grasselli, Philip Robin Loche, Matthias Linus Kellner, Sanggyu Chong, Filippo Bigi

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

2024

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Dirk Grundler, Andrea Mucchietto, Mingran Xu, Kyongmo An

Raw data and Python code for the manuscript "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution". All data files and source code are provided in a zip file. .ipynb file is a python jupyter notebook file whi ...

Zenodo2024

Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data

Sebastian Schulte

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the se ...

EPFL2024

Live-cell imaging powered by computation

Suliana Manley

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Nature Portfolio2024

Finding Actual Descent Directions For Adversarial Training

Volkan Cevher, Fabian Ricardo Latorre Gomez, Igor Krawczuk, Thomas Michaelsen Pethick, Leello Tadesse Dadi

Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal ad ...

2023

Search for CP violation in t(t)over-bar H and tH production in multilepton channels in proton-proton collisions at √s=13 TeV

Jian Wang, Matthias Finger, Qian Wang, Yiming Li, João Miguel das Neves Duarte, Matthias Wolf, Varun Sharma, Yi Zhang, Lei Zhang, Tian Cheng, Yixing Chen, Alexis Kalogeropoulos, Ioannis Papadopoulos, Hua Zhang, Siyuan Wang, Xin Chen, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Jian Zhao, Rakesh Chawla, Jan Steggemann, Konstantin Androsov, Anna Mascellani, Federica Legger, Matteo Galli, Gabriele Grosso

The charge-parity (CP) structure of the Yukawa interaction between the Higgs (H) boson and the top quark is measured in a data sample enriched in the t (t) over bar H and tH associated production, using 138 fb(-1) of data collected in proton-proton collisi ...

Springer Nature2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

A low-temperature prismatic slip instability in Mg understood using machine learning potentials

William Curtin, Xin Liu, Binglun Yin

Prismatic slip in magnesium at temperatures T

PERGAMON-ELSEVIER SCIENCE LTD2023

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

2023