| 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 | 3514 | 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 | 1748 | 2017 |
| 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 | 1078 | 2007 |
| A Hilbert space embedding for distributions A Smola, A Gretton, L Song, B Schölkopf International conference on algorithmic learning theory, 13-31, 2007 | 1029 | 2007 |
| 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 | 866 | 2018 |
| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International conference on machine learning, 2702-2711, 2016 | 848 | 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 | 770 | 2016 |
| 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 | 769 | 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 | 724 | 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 | 674 | 2018 |
| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 623 | 2023 |
| Stochastic training of graph convolutional networks with variance reduction J Chen, J Zhu, L Song arXiv preprint arXiv:1710.10568, 2017 | 612 | 2017 |
| 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 | 583 | 2017 |
| 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 | 520 | 2018 |
| Feature Selection via Dependence Maximization. L Song, A Smola, A Gretton, J Bedo, K Borgwardt Journal of Machine Learning Research 13 (5), 2012 | 497 | 2012 |
| 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 | 468 | 2007 |
| Learning to branch in mixed integer programming E Khalil, P Le Bodic, L Song, G Nemhauser, B Dilkina Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 461 | 2016 |
| 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 | 457 | 2013 |
| Syntax-directed variational autoencoder for structured data H Dai, Y Tian, B Dai, S Skiena, L Song arXiv preprint arXiv:1802.08786, 2018 | 405 | 2018 |
| 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 | 405 | 2009 |
Alex SmolaBoson AI在 smola.org 的电子邮件经过验证
