90 citations · 248 across the 15 of their papers we have counts for
5 papers · 1 filter
Efficient Matrix Implementation for Rotary Position Embedding
Chen Minqi, Zhongqi Yue, Shihao Zhang +5
Rotary Position Embedding (RoPE) has become a core component of modern Transformer architectures across language, vision, and 3D domains. However, existing implementations rely on…
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Haohan Wang, Zeyi Huang, Xindi Wu +1
Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of distributional shifts (e.g., rotations of…
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Zeyi Huang, Haohan Wang, Dong Huang +2
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in t…
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Haohan Wang, Zeyi Huang, Hanlin Zhang +2
Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. In this paper, we ai…
Squared Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Haohan Wang, Zeyi Huang, Xindi Wu +1
Data augmentation is one of the most popular techniques for improving the robustness of neural networks. In addition to directly training the model with original samples and augmen…