17 citations · 41 across the 6 of their papers we have counts for
6 papers
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
Sirui Liu, Jun Zhang, Haotian Chu +16
Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for…
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
Jiawei Zhou, Xiaoguang Li, Lifeng Shang +10
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However,…
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models
Daniel Gao, Yantao Jia, Lei Li +6
Previous works show the great potential of pre-trained language models (PLMs) for storing a large amount of factual knowledge. However, to figure out whether PLMs can be reliable k…
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
Xiang Huang, Hongsheng Liu, Beiji Shi +11
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a pro…
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Xiang Huang, Zhanhong Ye, Hongsheng Liu +11
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, bo…
Towards More Effective and Economic Sparsely-Activated Model
Hao Jiang, Ke Zhan, Jianwei Qu +14
The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become…