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Found 9 papers, 4 papers with code
Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-Training on Industrial-Scale Data
Extensive experiments have demonstrated that our framework can perform pre-training on real-world web-scale graphs with over 540 million nodes and 12 billion edges and generalizes effectively to unseen new graphs with different downstream tasks.
Improving Video Retrieval by Adaptive Margin
While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue.
Friend Ranking in Online Games via Pre-training Edge Transformers
Friend recall is an important way to improve Daily Active Users (DAU) in online games.
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations
To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
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A CLIP-Enhanced Method for Video-Language Understanding
This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark. github. io/challenge\_2021. html).
Initialization for Network Embedding: A Graph Partition Approach
Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification.
Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams.
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem.
