Fast Gumbel-Max Sketch and its Applications

no code implementations • 10 Feb 2023 • Yuanming Zhang, Pinghui Wang, Yiyan Qi, Kuankuan Cheng, Junzhou Zhao, Guangjian Tian, Xiaohong Guan

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval.

Information Retrieval Retrieval

Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images

no code implementations • 31 May 2022 • Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Jun Wang, Shihui Ying

In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.

Contrastive Learning Federated Learning +1

Fast Generating A Large Number of Gumbel-Max Variables

no code implementations • 2 Feb 2020 • Yiyan Qi, Pinghui Wang, Yuanming Zhang, Junzhou Zhao, Guangjian Tian, Xiaohong Guan

Instead of computing $k$ independent Gumbel random variables directly, we find that there exists a technique to generate these variables in descending order.

Graph Embedding Information Retrieval +1