| An analysis of single-layer networks in unsupervised feature learning A Coates, H Lee, AY Ng AISTATS, 2011 | 4890 | 2011 |
| Multimodal deep learning J Ngiam, A Khosla, M Kim, J Nam, H Lee, AY Ng Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 4307 | 2011 |
| Generative Adversarial Text to Image Synthesis S Reed, Z Akata, X Yan, L Logeswaran, B Schiele, H Lee arXiv preprint arXiv:1605.05396, 2016 | 4302 | 2016 |
| Learning structured output representation using deep conditional generative models K Sohn, H Lee, X Yan Advances in Neural Information Processing Systems, 3483-3491, 2015 | 3943 | 2015 |
| Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations H Lee, R Grosse, R Ranganath, AY Ng Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 3566 | 2009 |
| Efficient sparse coding algorithms H Lee, A Battle, R Raina, AY Ng Advances in neural information processing systems, 801-808, 2006 | 3566 | 2006 |
| A simple unified framework for detecting out-of-distribution samples and adversarial attacks K Lee, K Lee, H Lee, J Shin Advances in Neural Information Processing Systems, 7167-7177, 2018 | 2329 | 2018 |
| Self-taught learning: Transfer learning from unlabeled data R Raina, A Battle, H Lee, B Packer, AY Ng Proceedings of the 24th international conference on Machine learning, 759-766, 2007 | 2246 | 2007 |
| Deep learning for detecting robotic grasps I Lenz, H Lee, A Saxena The International Journal of Robotics Research 34 (4-5), 705-724, 2015 | 2018 | 2015 |
| Learning latent dynamics for planning from pixels D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson arXiv preprint arXiv:1811.04551, 2018 | 1721 | 2018 |
| Unsupervised feature learning for audio classification using convolutional deep belief networks H Lee, Y Largman, P Pham, AY Ng Advances in neural information processing systems, 2009 | 1594 | 2009 |
| Similarity of Neural Network Representations Revisited S Kornblith, M Norouzi, H Lee, G Hinton arXiv preprint arXiv:1905.00414, 2019 | 1523 | 2019 |
| Sparse deep belief net model for visual area V2 H Lee, C Ekanadham, A Ng Advances in neural information processing systems 20, 873-880, 2008 | 1380 | 2008 |
| Evaluation of Output Embeddings for Fine-Grained Image Classification Z Akata, S Reed, D Walter, H Lee, B Schiele CVPR, 2015 | 1285 | 2015 |
| Training Deep Neural Networks on Noisy Labels with Bootstrapping S Reed, H Lee, D Anguelov, C Szegedy, D Erhan, A Rabinovich arXiv preprint arXiv:1412.6596, 2014 | 1239 | 2014 |
| Data-Efficient Hierarchical Reinforcement Learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1805.08296, 2018 | 1095 | 2018 |
| Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin arXiv preprint arXiv:1711.09325, 2017 | 1075 | 2017 |
| Learning Deep Representations of Fine-Grained Visual Descriptions S Reed, Z Akata, H Lee, B Schiele Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 1074 | 2016 |
| Action-conditional video prediction using deep networks in atari games J Oh, X Guo, H Lee, RL Lewis, S Singh Advances in Neural Information Processing Systems, 2845-2853, 2015 | 1047 | 2015 |
| Learning What and Where to Draw S Reed, Z Akata, S Mohan, S Tenka, B Schiele, H Lee NIPS, 2016 | 947 | 2016 |
Andrew NgStanford UniversityVerified email at cs.stanford.edu
