24 citations · 33 across the 6 of their papers we have counts for
6 papers
Mini-Giants: "Small" Language Models and Open Source Win-Win
Zhengping Zhou, Lezhi Li, Xinxi Chen +1
ChatGPT is phenomenal. However, it is prohibitively expensive to train and refine such giant models. Fortunately, small language models are flourishing and becoming more and more c…
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models
Yuhui Zhang, Michihiro Yasunaga, Zhengping Zhou +4
Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data. In this work, we introduce NeQA,…
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Yuhui Zhang, Shih-Cheng Huang, Zhengping Zhou +2
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundament…
Physically Plausible Animation of Human Upper Body from a Single Image
Ziyuan Huang, Zhengping Zhou, Yung-Yu Chuang +2
We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate…
What and Where: A Context-based Recommendation System for Object Insertion
Song-Hai Zhang, Zhengping Zhou, Bin Liu +4
In this work, we propose a novel topic consisting of two dual tasks: 1) given a scene, recommend objects to insert, 2) given an object category, retrieve suitable background scenes…