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Computer Science

D-Index
56
Citations
42271
World Ranking
3931
National Ranking
1864

Overview

Ronan Collobert is affiliated with Facebook in the United States and specializes in research within the field of Computer Science. Their research output consists of 60 publications, with significant focus on subfields including Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Information Systems and Management, and Electrical and Electronic Engineering.

Their work centers primarily on topics such as Speech Recognition and Synthesis, Speech and Audio Processing, Music and Audio Processing, Natural Language Processing Techniques, Speech and Dialogue Systems, Domain Adaptation and Few-Shot Learning, and Topic Modeling.

Ronan Collobert has contributed to various recent publications, including:

  • Iterative Pseudo-Labeling for Speech Recognition, 2020, arXiv (Cornell University)
  • Pseudo-Labeling for Massively Multilingual Speech Recognition, 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Flashlight: Enabling Innovation in Tools for Machine Learning, 2022, arXiv (Cornell University)
  • Self-training and Pre-training are Complementary for Speech Recognition, 2020, arXiv (Cornell University)
  • CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings, 2021, arXiv (Cornell University)

The venues in which they frequently publish include:

  • arXiv (Cornell University)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

The scientist often collaborates with other researchers, with frequent coauthors including Tatiana Likhomanenko, Gabriel Synnaeve, Qiantong Xu, Vineel Pratap, and Navdeep Jaitly.

Best Publications

  • Natural Language Processing (Almost) from Scratch

    Ronan Collobert;Jason Weston;Léon Bottou;Michael Karlen

  • A unified architecture for natural language processing: deep neural networks with multitask learning

    Ronan Collobert;Jason Weston

  • Curriculum learning

    Yoshua Bengio;Jérôme Louradour;Ronan Collobert;Jason Weston

  • Torch7: A Matlab-like Environment for Machine Learning

    Ronan Collobert;Koray Kavukcuoglu;Clément Farabet

  • SVMTorch: support vector machines for large-scale regression problems

    Ronan Collobert;Samy Bengio

  • Deep Learning via Semi-Supervised Embedding

    Jason Weston;Frédéric Ratle;Hossein Mobahi;Ronan Collobert

  • wav2vec: Unsupervised Pre-Training for Speech Recognition.

    Steffen Schneider;Alexei Baevski;Ronan Collobert;Michael Auli

  • Learning structured embeddings of knowledge bases

    Antoine Bordes;Jason Weston;Ronan Collobert;Yoshua Bengio

  • Learning to Refine Object Segments

    Pedro Oliveira Pinheiro;Pedro Oliveira Pinheiro;Tsung-Yi Lin;Tsung-Yi Lin;Ronan Collobert;Piotr Dollár

  • Recurrent Convolutional Neural Networks for Scene Labeling

    Pedro Pinheiro;Ronan Collobert

  • From image-level to pixel-level labeling with Convolutional Networks

    Pedro O. Pinheiro;Ronan Collobert

  • Learning to segment object candidates

    Pedro O. Pinheiro;Ronan Collobert;Piotr Dollár

  • Torch: a modular machine learning software library

    Ronan Collobert;Samy Bengio;Johnny Mariéthoz

  • Large Scale Transductive SVMs

    Ronan Collobert;Fabian Sinz;Jason Weston;Léon Bottou

  • Unsupervised Cross-lingual Representation Learning for Speech Recognition

    Alexis Conneau;Alexei Baevski;Ronan Collobert;Abdelrahman Mohamed

  • Video (language) modeling: a baseline for generative models of natural videos.

    Marc'Aurelio Ranzato;Arthur Szlam;Joan Bruna;Michaël Mathieu

  • Deep learning via semi-supervised embedding

    Jason Weston;Frédéric Ratle;Ronan Collobert

  • Libri-Light: A Benchmark for ASR with Limited or No Supervision

    J. Kahn;M. Riviere;W. Zheng;E. Kharitonov

  • Trading convexity for scalability

    Ronan Collobert;Fabian Sinz;Jason Weston;Léon Bottou

  • Deep learning from temporal coherence in video

    Hossein Mobahi;Ronan Collobert;Jason Weston

  • A Parallel Mixture of SVMs for Very Large Scale Problems

    Ronan Collobert;Samy Bengio;Yoshua Bengio

Frequent Co-Authors

Jason Weston
Jason Weston Facebook (United States)
Gabriel Synnaeve
Gabriel Synnaeve Facebook (United States)
Léon Bottou
Léon Bottou Facebook (United States)
Yanjun Qi
Yanjun Qi University of Virginia
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Piotr Dollar
Piotr Dollar Facebook (United States)
Samy Bengio
Samy Bengio Apple (United States)
Yoshua Bengio
Yoshua Bengio University of Montreal
David Grangier
David Grangier Google (United States)
Michael Auli
Michael Auli Facebook (United States)

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