299 citations · 326 across the 8 of their papers we have counts for
8 papers
Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Zhuo Zuo, Li Yue, Wenhao Zheng +2
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: st…
Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation
Lilin Zhang, Yimo Guo, Yue Li +2
Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While adversarial training has become…
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
Xianggen Liu, Yan Guo, Haoran Li +4
Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision. Despite the emergence of diverse techniques to improve few-shot learn…
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
Wanlin Cai, Yuxuan Liang, Xianggen Liu +2
Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contr…
TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss
Site Mo, Haoxin Wang, Bixiong Li +3
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time. The real-world multivariate time series comes with n…
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Caiyang Yu, Xianggen Liu, Yifan Wang +5
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have ach…
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Pengyong Li, Jun Wang, Ziliang Li +6
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning o…
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
Xinke Shen, Xianggen Liu, Xin Hu +2
EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still p…