1 code implementation • ACL 2022 • Jue Wang, Ke Chen, Gang Chen, Lidan Shou, Julian McAuley
In this paper, we propose SkipBERT to accelerate BERT inference by skipping the computation of shallow layers.
no code implementations • 9 May 2025 • Yuxin Zhou, Zheng Li, Jun Zhang, Jue Wang, Yiping Wang, Zhongle Xie, Ke Chen, Lidan Shou
With the widespread adoption of Mixture-of-Experts (MoE) models, there is a growing demand for efficient inference on memory-constrained devices.
no code implementations • 24 Apr 2025 • Jun Zhang, Jue Wang, Huan Li, Zhongle Xie, Ke Chen, Lidan Shou
Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it individually may still be insufficient due to restricted data diversity and annotation budget.
1 code implementation • 22 Apr 2025 • Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen
With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task.
1 code implementation • 19 Feb 2025 • Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Yang You, Guiming Xie, Xuejian Gong, Kunlong Zhou
For a model with 70 billion parameters, LoRAM enables training on a GPU with only 20G HBM, replacing an A100-80G GPU for LoRA training and 15 GPUs for full fine-tuning.
no code implementations • 18 Feb 2025 • Jun Zhang, Huan Li, Dazhong Rong, Yan Zhao, Ke Chen, Lidan Shou
The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items.
2 code implementations • 2 Dec 2024 • Hong Lin, Shixin Wan, Zhongle Xie, Ke Chen, Meihui Zhang, Lidan Shou, Gang Chen
Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA).
no code implementations • 16 Oct 2024 • Yongqin Xu, Huan Li, Ke Chen, Lidan Shou
This study presents the Knowledge-Compliant Matching Framework (KcMF), an LLM-based approach that addresses these issues without the need for domain-specific fine-tuning.
no code implementations • 7 Mar 2024 • Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy.
1 code implementation • 15 Dec 2023 • Cheng Peng, Ke Chen, Lidan Shou, Gang Chen
The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data.
1 code implementation • 15 Sep 2023 • Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, Sharad Mehrotra
This approach is characterized by a two-stage process: drafting and verification.
1 code implementation • AAAI 2021 • Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Gang Chen
By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach.
no code implementations • 23 Nov 2020 • Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.
1 code implementation • 8 Oct 2020 • Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries.
Databases Data Structures and Algorithms
2 code implementations • ACL 2020 • Jue Wang, Lidan Shou, Ke Chen, Gang Chen
Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.
Ranked #1 on
Nested Named Entity Recognition
on NNE
no code implementations • 8 Apr 2019 • Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Sharad Mehrotra
In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself.