World's Best Scientists 2026 revealed!
Roland Memisevic

Roland Memisevic

D-Index & Metrics

Computer Science

D-Index
40
Citations
10233
World Ranking
9107
National Ranking
360

Overview

Roland Memisevic is affiliated with Twenty Billion Neurons in Canada and has contributed extensively to the field of computer science, with a focus on artificial intelligence. Their work spans topics such as topic modeling, natural language processing techniques, and human pose and action recognition.

Memisevic's research encompasses multiple subfields, including artificial intelligence, computer vision and pattern recognition, human-computer interaction, and cognitive neuroscience. Their published work also touches on multimodal machine learning applications, machine learning and data classification, computational physics and Python applications, as well as machine learning algorithms.

The scientist has authored multiple papers published primarily in arXiv (Cornell University), the most frequent venue for their work. Key recent publications include:

  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Deductive Verification of Chain-of-Thought Reasoning, 2023, arXiv (Cornell University)
  • Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving, 2023, arXiv (Cornell University)
  • Metaphors We Learn By, 2022, arXiv (Cornell University)
  • Is end-to-end learning enough for fitness activity recognition?, 2023, arXiv (Cornell University)

Frequent collaborators in their research include Sunny Panchal, M. Lee, Reza Pourreza, Apratim Bhattacharyya, and Ingo Bax, indicating a collaborative approach across several projects and topics.

The broad range of topics addressed by Memisevic illustrates engagement with machine learning challenges such as topic modeling, natural language processing, human activity recognition, and multimodal learning systems. Their publication record highlights contributions to advancing understanding in these areas through computational methods and interdisciplinary applications.

Best Publications

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • The “Something Something” Video Database for Learning and Evaluating Visual Common Sense

    Raghav Goyal;Samira Ebrahimi Kahou;Vincent Michalski;Joanna Materzynska

  • On Using Very Large Target Vocabulary for Neural Machine Translation

    Sébastien Jean;Kyunghyun Cho;Roland Memisevic;Yoshua Bengio

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Recurrent Neural Networks for Emotion Recognition in Video

    Samira Ebrahimi Kahou;Vincent Michalski;Kishore Konda;Roland Memisevic

  • Neural Networks with Few Multiplications

    Zhouhan Lin;Matthieu Courbariaux;Roland Memisevic;Yoshua Bengio

  • Learning to represent spatial transformations with factored higher-order boltzmann machines

    Roland Memisevic;Geoffrey E. Hinton

  • Unsupervised Learning of Image Transformations

    R. Memisevic;G. Hinton

  • The Jester Dataset: A Large-Scale Video Dataset of Human Gestures

    Joanna Materzynska;Guillaume Berger;Ingo Bax;Roland Memisevic

  • Denoising criterion for variational auto-encoding framework

    Daniel Im Jiwoong Im;Sungjin Ahn;Roland Memisevic;Yoshua Bengio

  • Generating images with recurrent adversarial networks

    Daniel Jiwoong Im;Chris Dongjoo Kim;Hui Jiang;Roland Memisevic

  • Learning Visual Odometry with a Convolutional Network

    Kishore Reddy Konda;Roland Memisevic

  • Montreal Neural Machine Translation Systems for WMT’15

    Sébastien Jean;Orhan Firat;Kyunghyun Cho;Roland Memisevic

  • Architectural Complexity Measures of Recurrent Neural Networks

    Saizheng Zhang;Yuhuai Wu;Tong Che;Zhouhan Lin

  • Learning to Relate Images

    R. Memisevic

  • Dropout as data augmentation

    Xavier Bouthillier;Kishore Konda;Pascal Vincent;Roland Memisevic

  • Learning to solve QBF

    Horst Samulowitz;Roland Memisevic

  • Gated Softmax Classification

    Roland Memisevic;Christopher Zach;Marc Pollefeys;Geoffrey E. Hinton

  • Modeling Deep Temporal Dependencies with Recurrent Grammar Cells

    Vincent Michalski;Roland Memisevic;Kishore Konda

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
David J. Fleet
David J. Fleet University of Toronto
Pascal Vincent
Pascal Vincent Facebook (United States)
Chris Pal
Chris Pal Polytechnique Montréal
Aaron Courville
Aaron Courville University of Montreal
Kyunghyun Cho
Kyunghyun Cho New York University
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Yann N. Dauphin
Yann N. Dauphin Google (United States)
Hui Jiang
Hui Jiang York University

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