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Computer Science

D-Index
39
Citations
154000
World Ranking
9452
National Ranking
374

Overview

Jimmy Ba is a researcher affiliated with the University of Toronto in Canada. Their work primarily spans the field of Computer Science, with a concentration on Artificial Intelligence, including subfields such as Computer Vision and Pattern Recognition, Molecular Biology, Management Science and Operations Research, and Civil and Structural Engineering.

The scientist's research topics cover a range of areas, including:

  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Reinforcement Learning in Robotics
  • Machine Learning and Data Classification
  • Natural Language Processing Techniques
  • Stochastic Gradient Optimization Techniques
  • Explainable Artificial Intelligence (XAI)

Their notable recent papers include:

  • Neuromodulatory Control Networks (NCNs): A Biologically Inspired Architecture for Dynamic LLM Processing, 2025, published in Zenodo (CERN European Organization for Nuclear Research)
  • Large Language Models Are Human-Level Prompt Engineers, 2022, published in arXiv (Cornell University)
  • BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning, 2020, published in arXiv (Cornell University)

Jimmy Ba frequently publishes in venues such as arXiv (Cornell University), where the majority of their work appears, as well as Zenodo (CERN European Organization for Nuclear Research), Nature, Molecular Systems Biology, and bioRxiv (Cold Spring Harbor Laboratory).

Collaboration is a significant component of their research. Frequent coauthors include:

  • Silviu Pitis
  • Keiran Paster
  • Danijar Hafner
  • Harris Chan
  • Michael R. Zhang

Best Publications

  • Adam: A Method for Stochastic Optimization

    Diederik P. Kingma;Jimmy Lei Ba

  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • Do Deep Nets Really Need to be Deep

    Jimmy Ba;Rich Caruana

  • Multiple Object Recognition with Visual Attention

    Jimmy Lei Ba;Volodymyr Mnih;Koray Kavukcuoglu

  • Layer Normalization

    Jimmy Lei Ba;Jamie Ryan Kiros;Geoffrey E. Hinton

  • Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

    Yuhuai Wu;Elman Mansimov;Shun Liao;Roger Grosse

  • Large Language Models Are Human-Level Prompt Engineers

    Unknown

  • Dream to Control: Learning Behaviors by Latent Imagination

    Danijar Hafner;Timothy Lillicrap;Jimmy Ba;Mohammad Norouzi

  • Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

    Yuhuai Wu;Elman Mansimov;Roger B. Grosse;Shun Liao

  • Classifying and segmenting microscopy images with deep multiple instance learning

    Oren Z. Kraus;Jimmy Lei Ba;Brendan J. Frey

  • Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions

    Jimmy Lei Ba;Kevin Swersky;Sanja Fidler;Ruslan Salakhutdinov

  • Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

    Emilio Parisotto;Jimmy Lei Ba;Ruslan Salakhutdinov

  • Lookahead Optimizer: k steps forward, 1 step back

    Michael R. Zhang;James Lucas;Geoffrey Hinton;Jimmy Ba

  • Adaptive dropout for training deep neural networks

    Jimmy Ba;Brendan Frey

  • Generating Images from Captions with Attention

    Elman Mansimov;Emilio Parisotto;Jimmy Lei Ba;Ruslan Salakhutdinov

  • Lookahead Optimizer: k steps forward, 1 step back

    Michael R. Zhang;James Lucas;Jimmy Ba;Geoffrey E. Hinton

  • Benchmarking Model-Based Reinforcement Learning

    Tingwu Wang;Xuchan Bao;Ignasi Clavera;Jerrick Hoang

  • Automated analysis of high-content microscopy data with deep learning.

    Oren Z Kraus;Ben T Grys;Jimmy Ba;Yolanda Chong

  • Mastering Diverse Domains through World Models

    Unknown

  • BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

    Yeming Wen;Dustin Tran;Jimmy Ba

  • NerveNet: Learning Structured Policy with Graph Neural Networks

    Tingwu Wang;Renjie Liao;Jimmy Ba;Sanja Fidler

Frequent Co-Authors

Roger Grosse
Roger Grosse University of Toronto
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Sanja Fidler
Sanja Fidler University of Toronto
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Brendan J. Frey
Brendan J. Frey University of Toronto
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Dustin Tran
Dustin Tran Google (United States)
Taiji Suzuki
Taiji Suzuki University of Tokyo
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)

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