2 citations · 3 across the 5 of their papers we have counts for
5 papers
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…
Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
Zhengde Zhang, Yiyu Zhang, Haodong Yao +10
Large Language Models (LLMs) are undergoing a period of rapid updates and changes, with state-of-the-art (SOTA) model frequently being replaced. When applying LLMs to a specific sc…
WeiPS: a symmetric fusion model framework for large-scale online learning
Xiang Yu, Fuping Chu, Junqi Wu +1
The recommendation system is an important commercial application of machine learning, where billions of feed views in the information flow every day. In reality, the interaction be…
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
Bo Huang, Ye Bi, Zhenyu Wu +2
The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user…
Syntactic sensitive complexity for symbol-free sequence
Cheng-Yuan Liou, Bo-Shiang Huang, Daw-Ran Liou +1
This work uses the L-system to construct a tree structure for the text sequence and derives its complexity. It serves as a measure of structural complexity of the text. It is appli…