| Describing videos by exploiting temporal structure L Yao, A Torabi, K Cho, N Ballas, C Pal, H Larochelle, A Courville Proceedings of the IEEE international conference on computer vision, 4507-4515, 2015 | 1338 | 2015 |
| Delving deeper into convolutional networks for learning video representations N Ballas, L Yao, C Pal, A Courville arXiv preprint arXiv:1511.06432, 2015 | 867 | 2015 |
| Generalized denoising auto-encoders as generative models Y Bengio, L Yao, G Alain, P Vincent Neural Information Processing Systems (NIPS) 2013, 2013 | 737 | 2013 |
| Learning to diagnose from scratch by exploiting dependencies among labels L Yao, E Poblenz, D Dagunts, B Covington, D Bernard, K Lyman arXiv preprint arXiv:1710.10501, 2017 | 428 | 2017 |
| Weakly supervised medical diagnosis and localization from multiple resolutions L Yao, J Prosky, E Poblenz, B Covington, K Lyman arXiv preprint arXiv:1803.07703, 2018 | 152 | 2018 |
| GSNs: generative stochastic networks G Alain, Y Bengio, L Yao, J Yosinski, E Thibodeau-Laufer, S Zhang, ... Information and Inference: A Journal of the IMA 5 (2), 210-249, 2016 | 76 | 2016 |
| Video description generation incorporating spatio-temporal features and a soft-attention mechanism L Yao, A Torabi, K Cho, N Ballas, C Pal, H Larochelle, A Courville arXiv preprint arXiv:1502.08029 6 (2), 201-211, 2015 | 61 | 2015 |
| Iterative Neural Autoregressive Distribution Estimator (NADE-k) T Raiko, L Yao, K Cho, Y Bengio Neural Information Processing Systems (NIPS) 2014, 2014 | 55 | 2014 |
| A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging KL Li Yao, Jordan Prosky, Ben Covington Medical Imaging with Deep Learning (MIDL), 2019 | 41* | 2019 |
| Bounding the test log-likelihood of generative models Y Bengio, L Yao, K Cho International Conference on Learning Representations (ICLR) 2013, arXiv …, 2013 | 26 | 2013 |
| On the Equivalence Between Deep NADE and Generative Stochastic Networks L Yao, S Ozair, K Cho, Y Bengio ECML/PKDD 2014, European Conference on Machine Learning and Principles and …, 2014 | 15 | 2014 |
| Multimodal Transitions for Generative Stochastic Networks S Ozair, L Yao, Y Bengio NIPS13 Deep learning workshop, arXiv preprint arXiv:1312.5578, 2013 | 10 | 2013 |
| Trainable performance upper bounds for image and video captioning L Yao, N Ballas, K Cho, JR Smith, Y Bengio, L Yao, N Ballas, K Cho, ... arXiv preprint arXiv 1511, 2015 | 6 | 2015 |
| Empirical performance upper bounds for image and video captioning L Yao, N Ballas, K Cho, JR Smith, Y Bengio | 2 | 2016 |
| Stacked calibration of off-policy policy evaluation for video game matchmaking E Laufer, RC Ferrari, L Yao, O Delalleau, Y Bengio 2013 IEEE Conference on Computational Inteligence in Games (CIG), 1-8, 2013 | 1 | 2013 |
| Anomaly detection and location with an application to an energy management system L Yao Aalto University, 2010 | 1 | 2010 |
| Caveats in Generating Medical Imaging Labels from Radiology Reports AU Tobi Olatunji, Li Yao, Ben Covington, Alexander Rhodes Medical Imaging with Deep Learning (MIDL), 2019 | | 2019 |
| Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction KL Nithya Attaluri, Ahmed Nasir, Carolynne Powe, Harold Racz, Ben Covington ... https://arxiv.org/abs/1810.00967, 2018 | | 2018 |
| Locating Anomalies Using Bayesian Factorizations and Masks. L Yao, A Lendasse, F Corona European Symposium on Artificial Neural Networks, Computational Intelligence …, 2011 | | 2011 |
| An Online Evaluation Platform for Proactive Information Retrieval Task L Yao, A Ajanki Pahikkala, Väyrynen, Kortela and Airola (eds.), 79, 2010 | | 2010 |
