Deep Learning
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
The recent availability of large datasets in bio-medicine has inspired the development of representation learning methods for multiple healthcare applications.
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks
However they remain commonly considered as less accurate, and their properties in learning are still not fully understood. In this paper we clarify the matter: when it comes to classification 1-Lipschitz neural networks enjoy several advantages over their unconstrained counterpart.
Improved Transformer for High-Resolution GANs Long Zhao
To further improve the performance, we introduce an additional self-modulation component based on cross-attention.
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models Xiuying Wei
Therefore, transformer quantization attracts wide research interest. Recent work recognizes that structured outliers are the critical bottleneck for quantization performance. However, their proposed methods increase the computation overhead and still leave the outliers there. To fundamentally address this problem, this paper delves into the inherent inducement and importance of the outliers.