Shengjie Wang

According to our database, Shengjie Wang authored at least 24 papers between 2015 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

DimCL: Dimension-Aware Augmentation in Contrastive Learning for Recommendation.

Chi Zhang, Qilong Han, Qiaoyu Tan, Shengjie Wang, Xiangyu Zhao, Rui Chen

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

2024

An End-to-End Submodular Framework for Data-Efficient In-Context Learning.

Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, Jeff A. Bilmes

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

2023

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation.

Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation.

Zhen Gong, Xin Wu, Lei Chen, Zhenzhe Zheng, Shengjie Wang, Anran Xu, Chong Wang, Fan Wu

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Machine Learning Force Fields with Data Cost Aware Training.

Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao

Proceedings of the International Conference on Machine Learning, 2023

PSLF: Defending Against Label Leakage in Split Learning.

Xinwei Wan, Jiankai Sun, Shengjie Wang, Lei Chen, Zhenzhe Zheng, Fan Wu, Guihai Chen

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

Retrospective Adversarial Replay for Continual Learning.

Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Constrained Robust Submodular Partitioning.

Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Robust Curriculum Learning: from clean label detection to noisy label self-correction.

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Proceedings of the 9th International Conference on Learning Representations, 2021

Curriculum Learning by Optimizing Learning Dynamics.

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Curriculum Learning by Dynamic Instance Hardness.

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Time-Consistent Self-Supervision for Semi-Supervised Learning.

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Jumpout : Improved Dropout for Deep Neural Networks with ReLUs.

Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes

Proceedings of the 36th International Conference on Machine Learning, 2019

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation.

Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes

Proceedings of the 36th International Conference on Machine Learning, 2019

Fixing Mini-batch Sequences with Hierarchical Robust Partitioning.

Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania, Jeff A. Bilmes

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Diverse Ensemble Evolution: Curriculum Data-Model Marriage.

Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Training Compressed Fully-Connected Networks with a Density-Diversity Penalty.

Shengjie Wang, Haoran Cai, Jeff A. Bilmes, William S. Noble

Proceedings of the 5th International Conference on Learning Representations, 2017

Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?

Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson

CoRR, 2016

Faster and more accurate graphical model identification of tandem mass spectra using trellises.

Shengjie Wang, John T. Halloran, Jeff A. Bilmes, William S. Noble

Bioinform., 2016

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix.

Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff A. Bilmes, Matthai Philipose, Matthew Richardson, Krzysztof J. Geras, Gregor Urban, Özlem Aslan

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation.

Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes

CoRR, 2015

Compressing LSTMs into CNNs.

Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Özlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton

CoRR, 2015

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications.

Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015