6 papers
DE-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
Jianing He, Qi Zhang, Weiping Ding +4
Early exiting has demonstrated its effectiveness in accelerating the inference of pre-trained language models like BERT by dynamically adjusting the number of layers executed. Howe…
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks
Jiaxing Miao, Liang Hu, Qi Zhang +1
Graph data in real-world scenarios undergo rapid and frequent changes, making it challenging for existing graph models to effectively handle the continuous influx of new data and a…
COLUR: Confidence-Oriented Learning, Unlearning and Relearning with Noisy-Label Data for Model Restoration and Refinement
Zhihao Sui, Liang Hu, Jian Cao +3
Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets wi…
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy
Zhihao Sui, Liang Hu, Jian Cao +4
Machine Unlearning (MU) technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology,…
Wills Aligner: Multi-Subject Collaborative Brain Visual Decoding
Guangyin Bao, Qi Zhang, Zixuan Gong +6
Decoding visual information from human brain activity has seen remarkable advancements in recent research. However, the diversity in cortical parcellation and fMRI patterns across…
MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correction
Zixuan Gong, Qi Zhang, Guangyin Bao +4
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks. Reconstructing high-qu…