4 papers
From Convolution to Transformer: A Comparative Study of U-Net Variants for Brain Tumor and Retinal Vessel Segmentation
Khoa Pham, Sindhuja Penchala, Jiacheng Li +2
Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used for biomedical image segmenta…
Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition
Gavin Money, Sindhuja Penchala, Jiacheng Li +1
Standard transformer architectures learn fixed slow-weight representations during training and lack mechanisms for rapid adaptation within an episode. In contrast, biological neura…
An Empirical Study of Position Bias in Modern Information Retrieval
Ziyang Zeng, Dun Zhang, Jiacheng Li +3
This study investigates the position bias in information retrieval, where models tend to overemphasize content at the beginning of passages while neglecting semantically relevant i…
Jasper and Stella: distillation of SOTA embedding models
Dun Zhang, Jiacheng Li, Ziyang Zeng +1
A crucial component in many deep learning applications, such as Frequently Asked Questions (FAQ) and Retrieval-Augmented Generation (RAG), is dense retrieval. In this process, embe…