An analysis of single-layer networks in unsupervised feature learning A Coates, H Lee, AY Ng AISTATS, 2011 | 4443 | 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 | 3977 | 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 | 3921 | 2016 |
Efficient sparse coding algorithms H Lee, A Battle, R Raina, AY Ng Advances in neural information processing systems, 801-808, 2006 | 3512 | 2006 |
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 | 3452 | 2009 |
Learning structured output representation using deep conditional generative models K Sohn, H Lee, X Yan Advances in Neural Information Processing Systems, 3483-3491, 2015 | 3389 | 2015 |
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 | 2192 | 2007 |
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 | 1941 | 2018 |
Deep learning for detecting robotic grasps I Lenz, H Lee, A Saxena The International Journal of Robotics Research 34 (4-5), 705-724, 2015 | 1848 | 2015 |
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 | 1559 | 2009 |
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 | 1470 | 2018 |
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 | 1359 | 2008 |
Evaluation of Output Embeddings for Fine-Grained Image Classification Z Akata, S Reed, D Walter, H Lee, B Schiele CVPR, 2015 | 1213 | 2015 |
Similarity of Neural Network Representations Revisited S Kornblith, M Norouzi, H Lee, G Hinton arXiv preprint arXiv:1905.00414, 2019 | 1177 | 2019 |
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 | 1142 | 2014 |
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 | 990 | 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 | 983 | 2015 |
Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin arXiv preprint arXiv:1711.09325, 2017 | 960 | 2017 |
Data-Efficient Hierarchical Reinforcement Learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1805.08296, 2018 | 941 | 2018 |
Learning What and Where to Draw S Reed, Z Akata, S Mohan, S Tenka, B Schiele, H Lee NIPS, 2016 | 898 | 2016 |