| Enhancing the reliability of out-of-distribution image detection in neural networks S Liang, Y Li, R Srikant 6th International Conference on Learning Representations (ICLR), 2018, 2017 | 2350 | 2017 |
| Why deep neural networks for function approximation? S Liang, R Srikant 5th International Conference on Learning Representations (ICLR), 2017, 2016 | 444 | 2016 |
| Adding One Neuron Can Eliminate All Bad Local Minima S Liang, R Sun, JD Lee, R Srikant Thirty-second Conference on Neural Information Processing Systems (NeurIPS …, 2018 | 101 | 2018 |
| The global landscape of neural networks: An overview R Sun, D Li, S Liang, T Ding, R Srikant IEEE Signal Processing Magazine 37 (5), 95-108, 2020 | 97 | 2020 |
| Understanding the loss surface of neural networks for binary classification S Liang, R Sun, Y Li, R Srikant Thirty-sixth International Conference on Machine Learning (ICML), 2018, 2018 | 90 | 2018 |
| Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv S Liang, Y Li, R Srikant arXiv preprint arXiv:1706.02690, 2017 | 24 | 2017 |
| Revisiting landscape analysis in deep neural networks: Eliminating decreasing paths to infinity S Liang, R Sun, R Srikant SIAM Journal on Optimization 32 (4), 2797-2827, 2022 | 16 | 2022 |
| The Role of Regularization in Overparameterized Neural Networks* S Satpathi, H Gupta, S Liang, R Srikant 2020 59th IEEE Conference on Decision and Control (CDC), 4683-4688, 2020 | 8 | 2020 |
| Graph out-of-distribution generalization with controllable data augmentation B Lu, Z Zhao, X Gan, S Liang, L Fu, X Wang, C Zhou IEEE Transactions on Knowledge and Data Engineering, 2024 | 6 | 2024 |
| Achieving small test error in mildly overparameterized neural networks S Liang, R Sun, R Srikant arXiv preprint arXiv:2104.11895, 2021 | 6 | 2021 |
| FINE: A framework for distributed learning on incomplete observations for heterogeneous crowdsensing networks L Fu, S Ma, L Kong, S Liang, X Wang IEEE/ACM Transactions on Networking 26 (3), 1092-1109, 2018 | 6 | 2018 |
| DataExpo: A One-Stop Dataset Service for Open Science Research B Lu, L Wu, L Yang, C Sun, W Liu, X Gan, S Liang, L Fu, X Wang, C Zhou Companion Proceedings of the ACM Web Conference 2023, 32-36, 2023 | 4 | 2023 |
| AceMap: Knowledge Discovery through Academic Graph X Wang, L Fu, X Gan, Y Wen, G Zheng, J Ding, L Xiang, N Ye, M Jin, ... arXiv preprint arXiv:2403.02576, 2024 | 3 | 2024 |
| FlowerCast: Efficient Time-Sensitive Multicast in Wireless Sensor Networks with Link Uncertainty J Tang, L Fu, S Liang, F Long, L Zhou, X Wang, C Zhou ACM Transactions on Sensor Networks 20 (1), 1-32, 2023 | 2 | 2023 |
| Temporal Generalization Estimation in Evolving Graphs B Lu, T Ma, X Gan, X Wang, Y Zhu, C Zhou, S Liang arXiv preprint arXiv:2404.04969, 2024 | 1 | 2024 |
| The role of explicit regularization in overparameterized neural networks S Liang University of Illinois at Urbana-Champaign, 2021 | 1 | 2021 |
| Archilles' Heel in Semi-open LLMs: Hiding Bottom against Recovery Attacks H Huang, Y Li, B Jiang, L Liu, R Sun, Z Liu, S Liang arXiv preprint arXiv:2410.11182, 2024 | | 2024 |
| Scientific and technological knowledge grows linearly over time H Kang, L Fu, RJ Funk, X Wang, J Ding, S Liang, J Wang, L Zhou, C Zhou arXiv preprint arXiv:2409.08349, 2024 | | 2024 |
| Hi-PART: Going Beyond Graph Pooling with Hierarchical Partition Tree for Graph-Level Representation Learning Y Ren, H Zhang, L Fu, S Liang, L Zhou, X Wang, X Cao, F Long, C Zhou ACM Transactions on Knowledge Discovery from Data 18 (4), 1-20, 2024 | | 2024 |
| Enhancing the Resilience of LLMs Against Grey-box Extractions H Huang, Y Li, B Jiang, B Jiang, L Liu, Z Liu, R Sun, S Liang ICML 2024 Next Generation of AI Safety Workshop, 2024 | | 2024 |
