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Found 5 papers, 3 papers with code
Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering
In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues.
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation
Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data.
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios.
STaSy: Score-based Tabular data Synthesis
Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training.
SOS: Score-based Oversampling for Tabular Data
To our knowledge, we are the first presenting a score-based tabular data oversampling method.
