World's Best Scientists 2026 revealed!
François Laviolette

François Laviolette

D-Index & Metrics

Computer Science

D-Index
33
Citations
13934
World Ranking
12349
National Ranking
478

Overview

François Laviolette was affiliated with Université Laval in Canada. Their research spanned multiple disciplines with a focus primarily in computer science and biochemistry, genetics, and molecular biology. Their work was situated at the intersection of artificial intelligence and molecular biology, among other subfields.

The scientist contributed extensively to areas including adversarial robustness in machine learning, muscle activation and electromyography studies, nutritional studies and diet, algorithms and data compression, genomics and phylogenetic studies, game theory applications, and safety systems engineering in autonomy.

The frequent collaborators throughout their career included Jacques Corbeil, Mazid Abiodoun Osseni, Maxime Déraspe, Paul H. Roy, and Josée Desharnais.

Their publications appeared repeatedly in several venues, with a notable number in arXiv (Cornell University), Scientific Reports, bioRxiv (Cold Spring Harbor Laboratory), Automated Software Engineering, and IEEE Access.

Notable recent publications include:

  • How to certify machine learning based safety-critical systems? A systematic literature review, 2022, Automated Software Engineering
  • Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition, 2020, IEEE Access
  • A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition, 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • Machine learning analysis identifies genes differentiating triple negative breast cancers, 2020, Scientific Reports
  • Are Machine Learning Algorithms More Accurate in Predicting Vegetable and Fruit Consumption Than Traditional Statistical Models? An Exploratory Analysis, 2022, Frontiers in Nutrition

Best Publications

  • Domain-adversarial training of neural networks

    Yaroslav Ganin;Evgeniya Ustinova;Hana Ajakan;Pascal Germain

  • Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

    Keith R. Bradnam;Joseph N. Fass;Anton Alexandrov;Paul Baranay

  • Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

    Keith R. Bradnam;Joseph N. Fass;Anton Alexandrov;Paul Baranay

  • Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

    Ulysse Cote-Allard;Cheikh Latyr Fall;Alexandre Drouin;Alexandre Campeau-Lecours

  • Ray Meta: scalable de novo metagenome assembly and profiling

    Sébastien Boisvert;Frédéric Raymond;Élénie Godzaridis;François Laviolette

  • Ray: simultaneous assembly of reads from a mix of high-throughput sequencing technologies.

    Sébastien Boisvert;François Laviolette;Jacques Corbeil

  • Domain-Adversarial Neural Networks

    Hana Ajakan;Pascal Germain;Hugo Larochelle;François Laviolette

  • PAC-Bayesian learning of linear classifiers

    Pascal Germain;Alexandre Lacasse;François Laviolette;Mario Marchand

  • Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

    Jacob L Jaremko;Marleine Azar;Rebecca Bromwich;Andrea Lum

  • Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.

    Ulysse Côté-Allard;Evan Campbell;Angkoon Phinyomark;François Laviolette

  • Transfer learning for sEMG hand gestures recognition using convolutional neural networks

    Ulysse Cote-Allard;Cheikh Latyr Fall;Alexandre Campeau-Lecours;Clement Gosselin

  • Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS.

    Pier-Luc Plante;Élina Francovic-Fontaine;Jody C May;John A McLean

  • A convolutional neural network for robotic arm guidance using sEMG based frequency-features

    Ulysse Cote Allard;Francois Nougarou;Cheikh Latyr Fall;Philippe Giguere

  • Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

    Alexandre Drouin;Sébastien Giguère;Maxime Déraspe;Mario Marchand

  • Approximate Analysis of Probabilistic Processes: Logic, Simulation and Games

    J. Desharnais;F. Laviolette;M. Tracol

  • Bisimulation and cocongruence for probabilistic systems

    Vincent Danos;Josée Desharnais;François Laviolette;Prakash Panangaden

  • PAC-Bayesian Inequalities for Martingales

    Y. Seldin;F. Laviolette;N. Cesa-Bianchi;J. Shawe-Taylor

  • Interpretable genotype-to-phenotype classifiers with performance guarantees.

    Alexandre Drouin;Gaël Letarte;Frédéric Raymond;Mario Marchand

  • Tighter PAC-Bayes bounds through distribution-dependent priors

    Guy Lever;François Laviolette;John Shawe-Taylor

  • PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier

    Alexandre Lacasse;François Laviolette;Mario Marchand;Pascal Germain

  • Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm

    Pascal Germain;Alexandre Lacasse;François Laviolette;Mario Marchand

  • Domain-Adversarial Training of Neural Networks.

    Yaroslav Ganin;Evgeniya Ustinova;Hana Ajakan;Pascal Germain

  • How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

    Florian Tambon;Gabriel Laberge;Le An;Amin Nikanjam

Frequent Co-Authors

Jacques Corbeil
Jacques Corbeil Université Laval
John Shawe-Taylor
John Shawe-Taylor University College London
Peter Auer
Peter Auer University of Leoben
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan
Erik Scheme
Erik Scheme University of New Brunswick
Angkoon Phinyomark
Angkoon Phinyomark University of New Brunswick
Hugo Larochelle
Hugo Larochelle Google (United States)
Nicolas Usunier
Nicolas Usunier Facebook (United States)
Daniel S. Rokhsar
Daniel S. Rokhsar University of California, Berkeley
Clément Gosselin
Clément Gosselin Université Laval

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