Lee, Honglak

Generative Adversarial Text to Image Synthesis

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 2,494

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 2,412

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

Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., et al. (2020) · Nature Medicine

Spectroscopy Techniques in Biomedical and Chemical ResearchAI in cancer detection

Cited by 790

Similarity of Neural Network Representations Revisited

Kornblith, S., Norouzi, M., Lee, H., & Hinton, G. (2019) · arXiv

Neural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 724

Learning Latent Dynamics for Planning from Pixels

Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 708

Decomposing Motion and Content for Natural Video Sequence Prediction

Villegas, R., Yang, J., Hong, S., Lin, X., & Lee, H. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisHuman Pose and Action Recognition

Cited by 488

Data-efficient hierarchical reinforcement learning

Nachum, O., Gu, S., Lee, H., & Levine, S. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 446

An efficient framework for learning sentence representations

Logeswaran, L., & Lee, H. (2018) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 427

Deep learning for robust feature generation in audiovisual emotion recognition

Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 401

Exploring the structure of a real-time, arbitrary neural artistic stylization network

Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., & Shlens, J. (2017) · Procedings of the British Machine Vision Conference 2017

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 306

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Buckman, J., Hafner, D., Tucker, G., Brevdo, E., & Lee, H. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 161

Deep Learning for Detecting Robotic Grasps

Lenz, I., Lee, H., & Saxena, A. (2013) · Robotics: Science and Systems IX

Robot Manipulation and LearningAdvanced Neural Network Applications

Cited by 126

Deep Variational Canonical Correlation Analysis

Wang, W., Yan, X., Lee, H., & Livescu, K. (2016) · arXiv

Statistical Methods and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 89

Improving Design Preference Prediction Accuracy Using Feature Learning

Burnap, A., Pan, Y., Liu, Y., Ren, Y., Lee, H., Gonzalez, R., & Papalambros, P. Y. (2016) · Journal of Mechanical Design

Product Development and CustomizationColor perception and design

Cited by 42

Environment Generation for Zero-Shot Compositional Reinforcement Learning

Gur, I., Jaques, N., Miao, Y., Choi, J., Tiwari, M., Lee, H., & Faust, A. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 12

Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

Hoang, C., Sohn, S., Choi, J., Carvalho, W., & Lee, H. (2021) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 11

Small Language Models Need Strong Verifiers to Self-Correct Reasoning

Zhang, Y., Khalifa, M., Logeswaran, L., Kim, J., Lee, M., Lee, H., & Wang, L. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 6

Small Language Models Need Strong Verifiers to Self-Correct Reasoning

Zhang, Y., Khalifa, M., Logeswaran, L., Kim, J., Lee, M., Lee, H., & Wang, L. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

A self-supervised framework for learning whole slide representations

Hou, X., Jiang, C., Kondepudi, A., Lyu, Y., Chowdury, A., Lee, H., & Hollon, T. C. (2024) · arXiv

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 4

A Definition of AGI

Hendrycks, D., Song, D., Szegedy, C., Lee, H., Gal, Y., Brynjolfsson, E., Li, S., Zou, A., Levine, L., Han, B., et al. (2025) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 3

Source-Aware Training Enables Knowledge Attribution in Language Models

Khalifa, M., Wadden, D., Strubell, E., Lee, H., Wang, L., Beltagy, I., & Peng, H. (2024) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 1