8 citations · 19 across the 10 of their papers we have counts for
11 papers
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
Zongye Hu, Weiqing Luo, Yanjie Fu +3
At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its c…
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation
Weiqing Luo, Zongye Hu, Xiao Wang +3
Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic relevance or surface-level sim…
Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval
Shijie Wang, Yadan Luo, Zijian Wang +2
Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervis…
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
Ziyi Huang, Henry Lam, Haofeng Zhang
Bayesian bandit algorithms with approximate Bayesian inference have been widely used in real-world applications. Despite the superior practical performance, their theoretical justi…
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but al…
Evaluating Aleatoric Uncertainty via Conditional Generative Models
Ziyi Huang, Henry Lam, Haofeng Zhang
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learn…
Cardiac Adipose Tissue Segmentation via Image-Level Annotations
Ziyi Huang, Yu Gan, Theresa Lye +5
Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of…
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Ziyi Huang, Henry Lam, Amirhossein Meisami +1
Bayesian bandit algorithms with approximate Bayesian inference have been widely used in real-world applications. However, there is a large discrepancy between the superior practica…
Quantifying Epistemic Uncertainty in Deep Learning
Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, espec…
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen, Ziyi Huang, Henry Lam +2
We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that mini…
Co-Seg: An Image Segmentation Framework Against Label Corruption
Ziyi Huang, Haofeng Zhang, Andrew Laine +3
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training. Neural networks can gradually overfit corrupted labels if directly tra…