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Found 5 papers, 1 papers with code
Temporal Logic Point Processes
We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.
Dynamic Sequential Graph Learning for Click-Through Rate Prediction
In this paper, we propose a novel method, named Dynamic Sequential Graph Learning (DSGL), to enhance users or items' representations by utilizing collaborative information from the local sub-graphs associated with users or items.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
Deep Interaction Processes for Time-Evolving Graphs
To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.
