76 citations · 181 across the 8 of their papers we have counts for
10 papers
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing
Lunyiu Nie, Jiuding Sun, Yanlin Wang +6
The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structur…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
Suppressing ZZ Crosstalk of Quantum Computers through Pulse and Scheduling Co-Optimization
Lei Xie, Jidong Zhai, Zhenxing Zhang +3
Noise is a significant obstacle to quantum computing, and crosstalk is one of the most destructive types of noise affecting superconducting qubits. Previous approaches to supp…
G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
Feng Zhang, Zaifeng Pan, Yanliang Zhou +4
Text analytics directly on compression (TADOC) has proven to be a promising technology for big data analytics. GPUs are extremely popular accelerators for data analytics systems. U…
FastMoE: A Fast Mixture-of-Expert Training System
Jiaao He, Jiezhong Qiu, Aohan Zeng +3
Mixture-of-Expert (MoE) presents a strong potential in enlarging the size of language model to trillions of parameters. However, training trillion-scale MoE requires algorithm and…
GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination
Tianhui Shi, Mingshu Zhai, Yi Xu +1
Graph pattern matching, which aims to discover structural patterns in graphs, is considered one of the most fundamental graph mining problems in many real applications. Despite pre…