212 citations · 306 across the 9 of their papers we have counts for
9 papers
Lightweight Gaussian Process Inference in C++ on Metal and CUDA
Yu-Hsueh Fang
Gaussian process (GP) inference in Python is dominated by libraries such as GPyTorch and GPflow, which are built on deep-learning frameworks and inherit their dispatch overhead and…
Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models
Mingze Wang, Shuchen Zhu, Yuxin Fang +3
Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the normalization operation has b…
Mixture-of-Depths Attention
Lianghui Zhu, Yuxin Fang, Bencheng Liao +10
Scaling depth is a key driver for large language models (LLMs). Yet, as LLMs become deeper, they often suffer from signal degradation: informative features formed in shallow layers…
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Quan Sun, Yuxin Fang, Ledell Wu +2
Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of mod…
EVA-02: A Visual Representation for Neon Genesis
Yuxin Fang, Quan Sun, Xinggang Wang +3
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.…
Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection
Yuxin Fang, Shusheng Yang, Shijie Wang +3
We present an approach to efficiently and effectively adapt a masked image modeling (MIM) pre-trained vanilla Vision Transformer (ViT) for object detection, which is based on our t…