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Found 6 papers, 2 papers with code
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes.
Fairly Adaptive Negative Sampling for Recommendations
Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i. e., clicked by a user) and negative items (i. e., obtained by negative sampling).
Disentangled Contrastive Learning for Social Recommendation
In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations DcRec.
Knowledge-enhanced Black-box Attacks for Recommendations
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
Transition Relation Aware Self-Attention for Session-based Recommendation
Specifically, TRASA first converts the session to a graph and then encodes the shortest path between items through the gated recurrent unit as their transition relation.
Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function.
