World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
36869
World Ranking
11859
National Ranking
4835

Overview

Myle Ott is a researcher affiliated with Facebook in the United States, specializing primarily in computer science with a focus on artificial intelligence. Their work spans several subfields including computer vision and pattern recognition, molecular biology, computer networks and communications, and hardware and architecture. The predominant areas of study relate to advanced computational techniques in artificial intelligence and related domains.

The main topics addressed in their research include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech Recognition and Synthesis
  • Multimodal Machine Learning Applications
  • Advanced Neural Network Applications
  • Parallel Computing and Optimization Techniques
  • Advanced Data Storage Technologies

Among the recent papers authored or coauthored by Myle Ott are:

  • "Towards Learning Terminological Concept Systems from Multilingual Natural Language Text," 2021, published at Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences," 2021, Proceedings of the National Academy of Sciences
  • "Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs," 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Sustainable AI: Environmental Implications, Challenges and Opportunities," 2021, arXiv (Cornell University)
  • "PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel," 2023, Proceedings of the VLDB Endowment

The frequent collaborators in their body of work include:

  • Sam Shleifer
  • Naman Goyal
  • Jingfei Du
  • Luke Zettlemoyer
  • Mikel Artetxe

Myle Ott's publications have appeared primarily in venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Proceedings of the National Academy of Sciences
  • Proceedings of the VLDB Endowment
  • Proceedings of the International AAAI Conference on Web and Social Media

Best Publications

  • RoBERTa: A Robustly Optimized BERT Pretraining Approach

    Yinhan Liu;Myle Ott;Naman Goyal;Jingfei Du

  • Unsupervised Cross-lingual Representation Learning at Scale

    Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

  • Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

    Alexander Rives;Alexander Rives;Joshua Meier;Tom Sercu;Siddharth Goyal

  • fairseq: A Fast, Extensible Toolkit for Sequence Modeling

    Myle Ott;Sergey Edunov;Alexei Baevski;Angela Fan

  • Finding Deceptive Opinion Spam by Any Stretch of the Imagination

    Myle Ott;Yejin Choi;Claire Cardie;Jeffrey T. Hancock

  • Understanding Back-Translation at Scale.

    Sergey Edunov;Myle Ott;Michael Auli;David Grangier

  • OPT: Open Pre-trained Transformer Language Models

    Unknown

  • Phrase-Based & Neural Unsupervised Machine Translation

    Guillaume Lample;Myle Ott;Alexis Conneau;Ludovic Denoyer

  • Recipes for building an open-domain chatbot

    Stephen Roller;Emily Dinan;Naman Goyal;Da Ju

  • Scaling Neural Machine Translation

    Myle Ott;Sergey Edunov;David Grangier;Michael Auli

  • Unsupervised Cross-lingual Representation Learning at Scale.

    Alexis Conneau;Kartikay Khandelwal;Naman Goyal;Vishrav Chaudhary

  • Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences

    Alexander Rives;Siddharth Goyal;Joshua Meier;Demi Guo

  • Towards a General Rule for Identifying Deceptive Opinion Spam

    Jiwei Li;Myle Ott;Claire Cardie;Eduard Hovy

  • Negative Deceptive Opinion Spam

    Myle Ott;Claire Cardie;Jeffrey T. Hancock

  • Estimating the prevalence of deception in online review communities

    Myle Ott;Claire Cardie;Jeff Hancock

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • Facebook FAIR’s WMT19 News Translation Task Submission

    Nathan Ng;Kyra Yee;Alexei Baevski;Myle Ott

  • Multi-aspect Sentiment Analysis with Topic Models

    Bin Lu;Myle Ott;Claire Cardie;Benjamin K. Tsou

  • Recipes for building an open-domain chatbot

    Stephen Roller;Emily Dinan;Naman Goyal;Da Ju

  • The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English

    Francisco Guzmán;Peng-Jen Chen;Myle Ott;Juan Miguel Pino

  • Few-shot Learning with Multilingual Generative Language Models

    Unknown

  • PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

    Unknown

  • fairseq: A Fast, Extensible Toolkit for Sequence Modeling.

    Myle Ott;Sergey Edunov;Alexei Baevski;Angela Fan

  • Classical Structured Prediction Losses for Sequence to Sequence Learning.

    Sergey Edunov;Myle Ott;Michael Auli;David Grangier

  • Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art.

    Patrick S. H. Lewis;Myle Ott;Jingfei Du;Veselin Stoyanov

  • Phrase-Based & Neural Unsupervised Machine Translation.

    Guillaume Lample;Myle Ott;Alexis Conneau;Ludovic Denoyer

Frequent Co-Authors

Marc'Aurelio Ranzato
Marc'Aurelio Ranzato DeepMind (United Kingdom)
Michael Auli
Michael Auli Facebook (United States)
Claire Cardie
Claire Cardie Cornell University
David Grangier
David Grangier Google (United States)
Jeffrey T. Hancock
Jeffrey T. Hancock Stanford University
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Veselin Stoyanov
Veselin Stoyanov Facebook (United States)
Alexis Conneau
Alexis Conneau Facebook (United States)
Luke Zettlemoyer
Luke Zettlemoyer University of Washington
Jason Weston
Jason Weston Facebook (United States)

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