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Found 6 papers, 4 papers with code
A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
Graph self-supervised learning is now a go-to method for pre-training graph foundation models, including graph neural networks, graph transformers, and more recent large language model (LLM)-based graph models.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape.
Masked Graph Autoencoder with Non-discrete Bandwidths
Inspired by these understandings, we explore non-discrete edge masks, which are sampled from a continuous and dispersive probability distribution instead of the discrete Bernoulli distribution.
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years.
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization.
Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training
Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.
