511 citations · 1.4k across the 31 of their papers we have counts for
10 papers · 1 filter
Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity
Weixin Liang, Junhong Shen, Genghan Zhang +3
State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling, but their inability to leverage modality-specific features limits their pe…
Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face
Xinyu Yang, Weixin Liang, James Zou
Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and trans…
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao +9
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scient…
OpenDataVal: a Unified Benchmark for Data Valuation
Kevin Fu Jiang, Weixin Liang, James Zou +1
Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data v…
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
Weixin Liang, Yining Mao, Yongchan Kwon +2
Understanding the performance of machine learning (ML) models across diverse data distributions is critically important for reliable applications. Despite recent empirical studies…
Data Budgeting for Machine Learning
Xinyi Zhao, Weixin Liang, James Zou
Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practi…
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
Weixin Liang, James Zou
Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing…
Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao, Yu Wang, Sai Li +4
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world app…
Neural Group Testing to Accelerate Deep Learning
Weixin Liang, James Zou
Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters. The sheer size of these networks imposes a challenging comput…
DAWSON: A Domain Adaptive Few Shot Generation Framework
Weixin Liang, Zixuan Liu, Can Liu
Training a Generative Adversarial Networks (GAN) for a new domain from scratch requires an enormous amount of training data and days of training time. To this end, we propose DAWSO…