Publications related to Interpreting chemisorption strength with AutoML-based feature deletion experiments

Related publications (16)

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

IEEE2024

Information Propagation in Multilayer Systems with Higher-Order Interactions across Timescales

Daniel Maria Busiello, Giorgio Nicoletti

Complex systems are characterized by multiple spatial and temporal scales. A natural framework to capture their multiscale nature is that of multilayer networks, where different layers represent distinct physical processes that often regulate each other in ...

American Physical Society2024

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Hong Zhang, Changpeng Lin

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...

NATURE PORTFOLIO2023

AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration

Mathieu Salzmann, Zheng Dang

In the current deep learning paradigm, the amount and quality of training data are as critical as the network architecture and its training details. However, collecting, processing, and annotating real data at scale is difficult, expensive, and time-consum ...

Ieee Computer Soc2023

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Silvestro Micera

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

IOP Publishing Ltd2023

A Multimodal Dataset for Automatic Edge-AI Cough Detection

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jérôme Paul Rémy Thevenot

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

New York2023

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Amer Chemical Soc2023

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

Qian Wang, Tobias Kober, Tom Hilbert

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

SPRINGER2022

Statistical distortion of supervised learning predictions in optical microscopy induced by image compression

Aleksandra Radenovic, David Nguyen, Arielle Louise Planchette

The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Investigating the effects of image compression on SL predictions is therefore pivotal to a ...

Nature Portfolio2022

Targeting the frontoparietal network using bifocal transcranial alternating current stimulation during a motor sequence learning task in healthy older adults

Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Laurijn Rianne Draaisma

Background: Healthy older adults show a decrease in motor performance and motor learning capacity as well as in working memory (WM) performance. WM has been suggested to be involved in motor learning processes, such as sequence learning. Correlational evid ...

2022

Decreasing the Stability of Borohydride with Ionic Liquids for Hydrogen Storage and Carbon Dioxide Conversion

Loris Giovanni Lombardo

The world needs to move from a fossil fuel to a renewable energy based society. With the increasing electricity production from wind and PV, short and long term energy storage are becoming one of challenge of the 21st century. While batteries are the prefe ...

EPFL2021

Deep Learning‐based Semantic Segmentation in Remote Sensing

Devis Tuia

Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear re ...

Wiley2021

Effect of the Impact Contact Duration on Machine Learning Models for Impact Position Detection

Yves Perriard, Paolo Germano, Jonathan André Jean-Marie Chavanne, Camilo Hernandez Mejia

Touch-screens have become the most common way of interaction between humans and machines. Nevertheless, the lack of rich haptic-feedback on most of the devices limits the quality and effectivity of the interaction. To develop novel haptic feedback generati ...

2020

Data-powered augmented volcano plots for homogeneous catalysis

Anne-Clémence Corminboeuf, Matthew Wodrich, Alberto Fabrizio, Benjamin André René Meyer

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithm ...

2020

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin

Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wa ...

2020

Analysis and Remediation of Handwriting difficulties

Thibault Lucien Christian Asselborn

Even with correct training, up to 25% of children never master handwriting like their peers. While research shows a correlation between handwriting difficulties and school failure, these difficulties can also impact children in their self esteem and behavi ...

EPFL2020