Sphereface: Deep hypersphere embedding for face recognition W Liu, Y Wen, Z Yu, M Li, B Raj, L Song Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2603 | 2017 |
Learning combinatorial optimization algorithms over graphs E Khalil, H Dai, Y Zhang, B Dilkina, L Song Advances in neural information processing systems 30, 2017 | 1143 | 2017 |
A Hilbert space embedding for distributions A Smola, A Gretton, L Song, B Schölkopf Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai …, 2007 | 888 | 2007 |
A kernel statistical test of independence A Gretton, K Fukumizu, C Teo, L Song, B Schölkopf, A Smola Advances in neural information processing systems 20, 2007 | 813 | 2007 |
Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International conference on machine learning, 2702-2711, 2016 | 654 | 2016 |
Recurrent marked temporal point processes: Embedding event history to vector N Du, H Dai, R Trivedi, U Upadhyay, M Gomez-Rodriguez, L Song Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 567 | 2016 |
Adversarial attack on graph structured data H Dai, H Li, T Tian, X Huang, L Wang, J Zhu, L Song International conference on machine learning, 1115-1124, 2018 | 543 | 2018 |
GRAM: graph-based attention model for healthcare representation learning E Choi, MT Bahadori, L Song, WF Stewart, J Sun Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 521 | 2017 |
Neural network-based graph embedding for cross-platform binary code similarity detection X Xu, C Liu, Q Feng, H Yin, L Song, D Song Proceedings of the 2017 ACM SIGSAC conference on computer and communications …, 2017 | 497 | 2017 |
Learning to explain: An information-theoretic perspective on model interpretation J Chen, L Song, M Wainwright, M Jordan International Conference on Machine Learning, 883-892, 2018 | 417 | 2018 |
Stochastic training of graph convolutional networks with variance reduction J Chen, J Zhu, L Song arXiv preprint arXiv:1710.10568, 2017 | 406 | 2017 |
Feature Selection via Dependence Maximization. L Song, A Smola, A Gretton, J Bedo, K Borgwardt Journal of Machine Learning Research 13 (5), 2012 | 400 | 2012 |
Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes K Zhou, H Zha, L Song Artificial Intelligence and Statistics, 641-649, 2013 | 397 | 2013 |
Supervised feature selection via dependence estimation L Song, A Smola, A Gretton, KM Borgwardt, J Bedo Proceedings of the 24th international conference on Machine learning, 823-830, 2007 | 396 | 2007 |
Variational reasoning for question answering with knowledge graph Y Zhang, H Dai, Z Kozareva, A Smola, L Song Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 341 | 2018 |
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs R Trivedi, H Dai, Y Wang, L Song international conference on machine learning, 3462-3471, 2017 | 333 | 2017 |
Estimating time-varying networks M Kolar, L Song, A Ahmed, EP Xing The Annals of Applied Statistics, 94-123, 2010 | 330 | 2010 |
Hilbert space embeddings of conditional distributions with applications to dynamical systems L Song, J Huang, A Smola, K Fukumizu Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 322 | 2009 |
Deep fried convnets Z Yang, M Moczulski, M Denil, N De Freitas, A Smola, L Song, Z Wang Proceedings of the IEEE international conference on computer vision, 1476-1483, 2015 | 320 | 2015 |
Scalable influence estimation in continuous-time diffusion networks N Du, L Song, M Gomez Rodriguez, H Zha Advances in neural information processing systems 26, 2013 | 311 | 2013 |