Publications related to An assessment of the structural resolution of various fingerprints commonly used in machine learning

Related publications (13)

Tiny satellites, big challenges: A feasibility study of machine vision pose estimation for PocketQubes during conjunctions

Niki Sajjad, Mathieu Salzmann, Andrew Lawrence Price

This paper assesses the feasibility of estimating the position and attitude of a target satellite using computer vision, not during proximity operations, but during conjunctions at distances greater than 1 km. The selected target is a small 5 × 5 × 5 cm3 P ...

2025

Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers

Madhur Panwar

Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could ...

MIT PRESS2025

Graph generative deep learning models with an application to circuit topologies

Igor Krawczuk

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Estimation of horizontal running power using foot-worn inertial measurement units

Kamiar Aminian, Mathieu Pascal Falbriard, Salil Apte

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed thr ...

FRONTIERS MEDIA SA2023

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Raimon Fabregat I De Aguilar-Amat

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powe ...

EPFL2022

Sensitivity analysis in core diagnostics

Mathieu Hursin

In the CORTEX project, methods to simulate neutron flux oscillations were enhanced and machine-learning based tools to determine the causes of measured neutron flux oscillations were developed, using the results of simulations as training and validation da ...

2022

Discourse Phenomena in Machine Translation

Lesly Sadiht Miculicich Werlen

Machine Translation (MT) has made considerable progress in the past two decades, particularly after the introduction of neural network models (NMT). During this time, the research community has mostly focused on modeling and evaluating MT systems at the se ...

EPFL2021

How Machine Learning is challenged by algorithmic complexity classes. From models to practices

Dominique Georges Léon Boullier

Machine Learning, or the learning methods used to constitute what is called Artificial Intelligence, is more diversified than is generally presented. Based in particular on the classification proposed by Domingos, the article begins by presenting different ...

SOC ANTHROPOLOGIE CONNAISSANCES2020

Deep Learning of Human Perception in Audio Event Classification

Samuel Denys Beuret

In this paper, we introduce our recent studies on human perception in audio event classification. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by and used as feature extractor for o ...

IEEE2018

Detecting EEG correlates during preparation of complex driving maneuvers

Lucian Andrei Gheorghe

The main focus of this research is the development of real-time recording and analysis devices and algorithms for detecting Electroencephalography (EEG) correlates of steering actions. First, offline analysis where performed in a high fidelity driving simu ...

EPFL2017

Learning Motion Dynamics to Catch a Moving Object

Aude Billard, Elena Gribovskaya, Seungsu Kim

In this paper, we consider a novel approach to control the timing of motions when these are encoded with autonomous dynamical systems (DS). Accurate timing of motion is crucial if a robot must synchronize its movement with that of a fast moving object. In ...

2010

Refining transcriptional regulatory networks using network evolutionary models and gene histories

Bernard Moret, Xiuwei Zhang

Background: Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for ...

2010