4 citations · 5 across the 6 of their papers we have counts for
6 papers
GQA-μP: The maximal parameterization update for grouped query attention
Kyle R. Chickering, Huijuan Wang, Mengxi Wu +7
Hyperparameter transfer across model architectures dramatically reduces the amount of compute necessary for tuning large language models (LLMs). The maximal update parameterization…
Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths
Xuezhe Ma, Shicheng Wen, Linghao Jin +11
Designing a unified neural network to efficiently and inherently process sequential data with arbitrary lengths is a central and challenging problem in sequence modeling. The desig…
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
Mengxi Wu, Hao Huang, Yi Fang +1
Unsupervised Domain Adaptation (UDA) is crucial for reducing the need for extensive manual data annotation when training deep networks on point cloud data. A significant challenge…
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning
Brad Windsor, Brandon O'Shea, Mengxi Wu
The Reinforcement Learning field is strong on achievements and weak on reapplication; a computer playing GO at a super-human level is still terrible at Tic-Tac-Toe. This paper asks…
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data
Mengxi Wu, Mohammad Rostami
Graph-structured data can be found in numerous domains, yet the scarcity of labeled instances hinders its effective utilization of deep learning in many scenarios. Traditional unsu…
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation
Mengxi Wu, Hao Huang, Yi Fang
With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D po…