1 citations · 1 across the 5 of their papers we have counts for
6 papers
A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports
Jia Yu, Yan Zhu, Yili He +12
Vision-language models remain underused in colonoscopy despite the rich expert descriptions recorded in routine reports. These reports document lesion appearance, size and location…
MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation
Qihang Yu, Kairui Fu, Zhaocheng Du +10
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both i…
CHORD: Customizing Hybrid-precision On-device Model for Sequential Recommendation with Device-cloud Collaboration
Tianqi Liu, Kairui Fu, Shengyu Zhang +5
With the advancement of mobile device capabilities, deploying reranking models directly on devices has become feasible, enabling real-time contextual recommendations. When migratin…
Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models
Jia Yu, Yan Zhu, Peiyao Fu +8
Colorectal cancer (CRC) is a significant global health concern, and early detection through screening plays a critical role in reducing mortality. While deep learning models have s…
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
Chengyuan Liu, Yangyang Kang, Shihang Wang +5
The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). Howe…
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
Feng Zhu, Mingjie Zhong, Xinxing Yang +8
In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-…