298 citations · 716 across the 23 of their papers we have counts for
10 papers · 1 filter
GLM-130B: An Open Bilingual Pre-trained Model
Aohan Zeng, Xiao Liu, Zhengxiao Du +15
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good…
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
Xiao Liu, Shiyu Zhao, Kai Su +6
Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs. However, limited by their inherently shallow and static architectures, they can h…
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
Weng Lam Tam, Xiao Liu, Kaixuan Ji +6
Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both languag…
Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge
Rui He, Yuanxi Sun, Youzeng Li +4
Generic Event Boundary Detection (GEBD) tasks aim at detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. In this paper, we apply Masked Autoen…
GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu, Lianmin Zheng, Dequan Wang +9
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.…
Rethinking the Setting of Semi-supervised Learning on Graphs
Ziang Li, Ming Ding, Weikai Li +4
We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In th…
GraphMAE: Self-Supervised Masked Graph Autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen +4
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly, generative SSL has seen emerging success in natural language processing and other AI fie…
GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang +4
Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. A recent study shows that the graph random neural network (GRAND) model can generate s…
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
Xiao Liu, Haoyun Hong, Xinghao Wang +4
Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its de…
Training Free Graph Neural Networks for Graph Matching
Zhiyuan Liu, Yixin Cao, Fuli Feng +4
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution wit…