World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
91
Citations
52175
World Ranking
561
National Ranking
298

Overview

Honglak Lee is affiliated with the University of Michigan-Ann Arbor in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and its applications.

The research work by Honglak Lee spans multiple main topics, including:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Reinforcement Learning in Robotics
  • Natural Language Processing Techniques
  • Topic Modeling
  • Cell Image Analysis Techniques
  • Generative Adversarial Networks and Image Synthesis

Lee's primary fields of study encompass computer science with subfields in:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Biophysics
  • Radiology, Nuclear Medicine and Imaging
  • Statistical and Nonlinear Physics

The frequent collaborators who have co-authored works with Lee include:

  • Sungryull Sohn
  • Lajanugen Logeswaran
  • Todd Hollon
  • Akhil Kondepudi
  • Moontae Lee

Lee has published in a variety of reputable venues. Among the frequent publication platforms are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neuro-Oncology
  • Nature Medicine
  • Scientific Reports

Representative recent papers authored or co-authored by Lee include:

  • Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks, 2020, Nature Medicine
  • Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging, 2023, Nature Medicine
  • Improved Consistency Regularization for GANs, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution, 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Pure Transformers are Powerful Graph Learners, 2022, arXiv (Cornell University)

Best Publications

  • Efficient sparse coding algorithms

    Honglak Lee;Alexis Battle;Rajat Raina;Andrew Y. Ng

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • An analysis of single-layer networks in unsupervised feature learning

    Adam Coates;Andrew Y. Ng;Honglak Lee

  • Multimodal Deep Learning

    Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam

  • Generative adversarial text to image synthesis

    Scott Reed;Zeynep Akata;Xinchen Yan;Lajanugen Logeswaran

  • Learning structured output representation using deep conditional generative models

    Kihyuk Sohn;Xinchen Yan;Honglak Lee

  • Self-taught learning: transfer learning from unlabeled data

    Rajat Raina;Alexis Battle;Honglak Lee;Benjamin Packer

  • Deep learning for detecting robotic grasps

    Ian Lenz;Honglak Lee;Ashutosh Saxena

  • Unsupervised feature learning for audio classification using convolutional deep belief networks

    Honglak Lee;Peter Pham;Yan Largman;Andrew Y. Ng

  • Sparse deep belief net model for visual area V2

    Honglak Lee;Chaitanya Ekanadham;Andrew Y. Ng

  • Evaluation of output embeddings for fine-grained image classification

    Zeynep Akata;Scott Reed;Daniel Walter;Honglak Lee

  • A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

    Kimin Lee;Kibok Lee;Honglak Lee;Jinwoo Shin

  • Learning Deep Representations of Fine-Grained Visual Descriptions

    Scott Reed;Zeynep Akata;Honglak Lee;Bernt Schiele

  • Attribute2Image: Conditional Image Generation from Visual Attributes

    Xinchen Yan;Jimei Yang;Kihyuk Sohn;Honglak Lee

  • TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING

    Scott E. Reed;Honglak Lee;Dragomir Anguelov;Christian Szegedy

  • Action-conditional video prediction using deep networks in Atari games

    Junhyuk Oh;Xiaoxiao Guo;Honglak Lee;Richard Lewis

  • Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

    Todd C. Hollon;Balaji Pandian;Arjun R. Adapa;Esteban Urias

  • Learning Latent Dynamics for Planning from Pixels

    Danijar Hafner;Timothy P. Lillicrap;Ian Fischer;Ruben Villegas

  • Data-Efficient Hierarchical Reinforcement Learning

    Ofir Nachum;Shixiang Gu;Honglak Lee;Sergey Levine

  • Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

    Kimin Lee;Honglak Lee;Kibok Lee;Jinwoo Shin

  • Learning Deep Representations of Fine-grained Visual Descriptions

    Scott Reed;Zeynep Akata;Bernt Schiele;Honglak Lee

  • Zero-Shot Learning with Structured Embeddings

    Zeynep Akata;Honglak Lee;Bernt Schiele

Frequent Co-Authors

Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Kihyuk Sohn
Kihyuk Sohn Google (United States)
Satinder Singh
Satinder Singh DeepMind (United Kingdom)
Andrew Y. Ng
Andrew Y. Ng Stanford University
Jimei Yang
Jimei Yang Adobe Systems (United States)
Dumitru Erhan
Dumitru Erhan Google (United States)
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Richard L. Lewis
Richard L. Lewis University of Michigan–Ann Arbor
Zeynep Akata
Zeynep Akata University of Tübingen
Dragomir R. Radev
Dragomir R. Radev Yale University

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