World's Best Scientists 2026 revealed!
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Computer Science
Czechia
2025

D-Index & Metrics

Computer Science

D-Index
43
Citations
121605
World Ranking
7724
National Ranking
12

Research.com Recognitions

  • 2025 - Research.com Computer Science in Czechia Leader Award
  • 2022 - Research.com Computer Science in Czechia Leader Award

Overview

Tomas Mikolov is affiliated with the Czech Technical University in Prague, Czech Republic. Their research primarily spans the field of Computer Science, with a strong focus on Artificial Intelligence and related subfields such as Computational Theory and Mathematics, Molecular Biology, Astronomy and Astrophysics, and Mechanical Engineering.

The scientist's work involves several main topics including Cellular Automata and Applications, Natural Language Processing Techniques, Topic Modeling, DNA and Biological Computing, Computability, Logic, AI Algorithms, Origins and Evolution of Life, and Modular Robots and Swarm Intelligence.

Recent published papers by Tomas Mikolov include the following:

  • Special Issue "On Defining Artificial Intelligence"-Commentaries and Author's Response, 2020, Journal of Artificial General Intelligence
  • Evaluating Online Continual Learning with CALM, 2020, arXiv (Cornell University)
  • Emergence of Self-Reproducing Metabolisms as Recursive Algorithms in an Artificial Chemistry, 2021, Artificial Life
  • Benchmarking Learning Efficiency in Deep Reservoir Computing, 2022, arXiv (Cornell University)
  • Collapse of Self-trained Language Models, 2024, arXiv (Cornell University)

Tomas Mikolov frequently collaborates with a number of coauthors, including:

  • David Herel
  • Barbora Hudcová
  • Germán Kruszewski
  • Hugo Cisneros
  • Stefano Nichele

The scientist has published notably in venues such as arXiv (Cornell University), where most of their work appears, as well as in Artificial Life, and the Journal of Artificial General Intelligence.

Best Publications

  • Distributed Representations of Words and Phrases and their Compositionality

    Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado

  • Efficient Estimation of Word Representations in Vector Space

    Tomas Mikolov;Kai Chen;Greg S. Corrado;Jeffrey Dean

  • Enriching Word Vectors with Subword Information

    Piotr Bojanowski;Edouard Grave;Armand Joulin;Tomas Mikolov

  • Distributed Representations of Sentences and Documents

    Quoc Le;Tomas Mikolov

  • Recurrent neural network based language model

    Tomas Mikolov;Martin Karafiát;Lukás Burget;Jan Cernocký

  • On the difficulty of training recurrent neural networks

    Razvan Pascanu;Tomas Mikolov;Yoshua Bengio

  • Extensions of recurrent neural network language model

    Tomas Mikolov;Stefan Kombrink;Lukas Burget;Jan Cernocky

  • Bag of Tricks for Efficient Text Classification

    Armand Joulin;Edouard Grave;Piotr Bojanowski;Tomas Mikolov

  • Linguistic Regularities in Continuous Space Word Representations

    Tomas Mikolov;Wen-tau Yih;Geoffrey Zweig

  • DeViSE: A Deep Visual-Semantic Embedding Model

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

  • Exploiting Similarities among Languages for Machine Translation

    Tomas Mikolov;Quoc V. Le;Ilya Sutskever

  • Advances in Pre-Training Distributed Word Representations

    Tomas Mikolov;Edouard Grave;Piotr Bojanowski;Christian Puhrsch

  • Learning Word Vectors for 157 Languages

    Edouard Grave;Piotr Bojanowski;Prakhar Gupta;Armand Joulin

  • FastText.zip: Compressing text classification models

    Armand Joulin;Edouard Grave;Piotr Bojanowski;Matthijs Douze

  • Zero-Shot Learning by Convex Combination of Semantic Embeddings

    Mohammad Norouzi;Tomas Mikolov;Samy Bengio;Yoram Singer

  • Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

    Jason Weston;Antoine Bordes;Sumit Chopra;Alexander M. Rush

  • Context dependent recurrent neural network language model

    Tomas Mikolov;Geoffrey Zweig

  • Strategies for training large scale neural network language models

    Tomas Mikolov;Anoop Deoras;Daniel Povey;Lukas Burget

  • Understanding the exploding gradient problem

    Razvan Pascanu;Tomas Mikolov;Yoshua Bengio

  • One billion word benchmark for measuring progress in statistical language modeling.

    Ciprian Chelba;Tomas Mikolov;Mike Schuster;Qi Ge

Frequent Co-Authors

Armand Joulin
Armand Joulin Google (United States)
Edouard Grave
Edouard Grave Facebook (United States)
Piotr Bojanowski
Piotr Bojanowski Facebook (United States)
Lukas Burget
Lukas Burget Brno University of Technology
Jeffrey Dean
Jeffrey Dean Google (United States)
Greg Corrado
Greg Corrado Google (United States)
Josef Sivic
Josef Sivic Czech Technical University in Prague
Marco Baroni
Marco Baroni Institució Catalana de Recerca i Estudis Avançats
Marc'Aurelio Ranzato
Marc'Aurelio Ranzato DeepMind (United Kingdom)
Martin Karafiat
Martin Karafiat Brno University of Technology

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