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Computer Science

D-Index
63
Citations
42336
World Ranking
2670
National Ranking
156

Overview

Marc'Aurelio Ranzato is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, focusing on several interconnected subfields.

Their main areas of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Developmental and Educational Psychology
  • Computational Theory and Mathematics

Marc'Aurelio Ranzato's work covers a broad spectrum of topics in machine learning and related disciplines:

  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning
  • Natural Language Processing Techniques
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Machine Learning and ELM

Their publication record includes papers in multiple venues, notably:

  • arXiv (Cornell University)
  • Trends in Cognitive Sciences
  • Transactions of the Association for Computational Linguistics
  • Dagstuhl Research Online Publication Server

Examples of recent papers by Marc'Aurelio Ranzato include:

  • The <scp>Flores-101</scp> Evaluation Benchmark for Low-Resource and Multilingual Machine Translation, 2022, Transactions of the Association for Computational Linguistics
  • The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation, 2021, arXiv (Cornell University)
  • Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions, 2021, arXiv (Cornell University)
  • Efficient Continual Learning with Modular Networks and Task-Driven Priors, 2020, arXiv (Cornell University)
  • Helpless infants are learning a foundation model, 2024, Trends in Cognitive Sciences

They frequently collaborate with several co-authors, including:

  • Arthur Szlam
  • Arthur Douillard
  • Jiajun Shen
  • Myle Ott
  • Andrei A. Rusu

Best Publications

  • DeepFace: Closing the Gap to Human-Level Performance in Face Verification

    Yaniv Taigman;Ming Yang;Marc'Aurelio Ranzato;Lior Wolf

  • Large Scale Distributed Deep Networks

    Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen

  • What is the best multi-stage architecture for object recognition?

    Kevin Jarrett;Koray Kavukcuoglu;Marc'Aurelio Ranzato;Yann LeCun

  • Building high-level features using large scale unsupervised learning

    Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen

  • DeViSE: A Deep Visual-Semantic Embedding Model

    Andrea Frome;Greg S Corrado;Jon Shlens;Samy Bengio

  • Efficient Learning of Sparse Representations with an Energy-Based Model

    Marc'aurelio Ranzato;Christopher Poultney;Sumit Chopra;Yann L. Cun

  • Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

    M.A. Ranzato;Fu Jie Huang;Y.-L. Boureau;Yann LeCun

  • Sequence Level Training with Recurrent Neural Networks

    Marc'Aurelio Ranzato;Sumit Chopra;Michael Auli;Wojciech Zaremba

  • Word translation without parallel data

    Guillaume Lample;Alexis Conneau;Marc'Aurelio Ranzato;Ludovic Denoyer

  • Gradient Episodic Memory for Continual Learning

    David Lopez-Paz;Marc'Aurelio Ranzato

  • Sparse Feature Learning for Deep Belief Networks

    Marc'aurelio Ranzato;Y-lan Boureau;Yann L. Cun

  • Predicting Parameters in Deep Learning

    Misha Denil;Babak Shakibi;Laurent Dinh;Marc'Aurelio Ranzato

  • Efficient Lifelong Learning with A-GEM

    Arslan Chaudhry;Marc'Aurelio Ranzato;Marcus Rohrbach;Mohamed Elhoseiny

  • On rectified linear units for speech processing

    M. D. Zeiler;M. Ranzato;R. Monga;M. Mao

  • Phrase-Based & Neural Unsupervised Machine Translation

    Guillaume Lample;Myle Ott;Alexis Conneau;Ludovic Denoyer

  • Unsupervised Machine Translation Using Monolingual Corpora Only

    Guillaume Lample;Alexis Conneau;Ludovic Denoyer;Marc'Aurelio Ranzato

  • PANDA: Pose Aligned Networks for Deep Attribute Modeling

    Ning Zhang;Manohar Paluri;Marc'Aurelio Ranzato;Trevor Darrell

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

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

  • Fader Networks:Manipulating Images by Sliding Attributes

    Guillaume Lample;Guillaume Lample;Neil Zeghidour;Nicolas Usunier;Antoine Bordes

  • Learning invariant features through topographic filter maps

    Koray Kavukcuoglu;Marc Aurelio Ranzato;Rob Fergus;Yann Le-Cun

  • On Tiny Episodic Memories in Continual Learning

    Arslan Chaudhry;Marcus Rohrbach;Mohamed Elhoseiny;Thalaiyasingam Ajanthan

Frequent Co-Authors

Myle Ott
Myle Ott Facebook (United States)
Ludovic Denoyer
Ludovic Denoyer Sorbonne University
Sumit Chopra
Sumit Chopra New York University
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Michael Auli
Michael Auli Facebook (United States)
Yann LeCun
Yann LeCun Facebook (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Andrew W. Senior
Andrew W. Senior Google (United States)

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