Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.
Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.
Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives.
The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
1 code implementation • 1 Mar 2021 • Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
1 code implementation • 10 Jan 2021 • Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingyuan Ma, Zhaoyang Shen, Juejian Wu, Yuanfan Xu, Hengrui Zhang, Kai Zhong, Xuefei Ning, Yuzhe ma, HaoYu Yang, Bei Yu, Huazhong Yang, Yu Wang
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods.
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