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Guanghui Qin

9 accepted papers

2026

Masked-Diffusion Autoencoders for 3D Medical Vision Representation Learning

CVPR 2026

Effective medical image analysis requires representations that capture both global anatomical structure and fine-grained tissue texture. Current self-supervised approaches exhibit limited capacity to address both requirements simultaneously. Invariance-based methods learn through augmentation consis

Cited by 0SourceScholar
2026

OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning

CVPR 2026

High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tasks. We investigate strategies for training and data curation to develop a robust multimodal reasoning model in the medic

Cited by 0SourceScholar
2025

CLERC: A Dataset for U. S. Legal Case Retrieval and Retrieval-Augmented Analysis Generation

NAACL 2025findings

Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligence systems assisting legal professionals in writing such documents provide great benefits but are challenging to design. Such systems need to help locate, summarize, and…

2024

Dodo: Dynamic Contextual Compression for Decoder-only LMs

ACL 2024long

Transformer-based language models (LMs) are inefficient in long contexts. We propose Dodo, a solution for context compression. Instead of one vector per token in a standard transformer model, Dodo represents text with a dynamic number of hidden states at each layer, reducing the cost of self-attenti…

Cited by 7SourcePDFScholar
2021

Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

EMNLP 2021main

Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of “train on Engli…

2020

Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification

ICML 2020poster

Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large. Training an unrestricted neural model might overfit to spurious patterns. To exploit domain-specific knowledge of how past events might affect an event’s present probability…