20 citations · 73 across the 12 of their papers we have counts for
12 papers
Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting
Guande He, Peng Cui, Jianfei Chen +2
Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-…
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Jiaxin Gao, Wenbo Hu, Yuntian Chen
Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems. Whil…
Iterative Adversarial Attack on Image-guided Story Ending Generation
Youze Wang, Wenbo Hu, Richang Hong
Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial asp…
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
Wenbo Hu, Xin Sun, Qiang liu +2
Post-click conversion rate (CVR) is a reliable indicator of online customers' preferences, making it crucial for developing recommender systems. A major challenge in predicting CVR…
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Jiaxin Gao, Wenbo Hu, Dongxiao Zhang +1
Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.…
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Yingtao Luo, Qiang Liu, Yuntian Chen +3
Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics…
StackVAE-G: An efficient and interpretable model for time series anomaly detection
Wenkai Li, Wenbo Hu, Ting Chen +2
Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsuper…
Accurate and Reliable Forecasting using Stochastic Differential Equations
Peng Cui, Zhijie Deng, Wenbo Hu +1
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. Although a lot of efforts have been made,…
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Qingyi Pan, Wenbo Hu, Jun Zhu
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to impro…
Dynamic Window-level Granger Causality of Multi-channel Time Series
Zhiheng Zhang, Wenbo Hu, Tian Tian +1
Granger causality method analyzes the time series causalities without building a complex causality graph. However, the traditional Granger causality method assumes that the causali…
Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui, Wenbo Hu, Jun Zhu
Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertai…
Big Learning with Bayesian Methods
Jun Zhu, Jianfei Chen, Wenbo Hu +1
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning…