7 papers
Disentangled Representation Learning for Causal Inference with Instruments
Debo Cheng, Jiuyong Li, Lin Liu +4
Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this chal…
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
Wei Tang, Weijia Zhang, Min-Ling Zhang
Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing…
A Comparative Analysis of Faithfulness Metrics and Humans in Citation Evaluation
Weijia Zhang, Mohammad Aliannejadi, Jiahuan Pei +3
Large language models (LLMs) often generate content with unsupported or unverifiable content, known as "hallucinations." To address this, retrieval-augmented LLMs are employed to i…
Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
Weijia Zhang, Jia-Hong Huang, Svitlana Vakulenko +3
Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional…
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
Weijia Zhang, Dongnan Liu, Chao Ma +1
Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in autonomous driving due to absence of explicit depth cues in a single RGB image. In this pap…
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning
Hanwen Deng, Weijia Zhang, Min-Ling Zhang
Label noise widely exists in large-scale datasets and significantly degenerates the performances of deep learning algorithms. Due to the non-identifiability of the instance-depende…