Interest-Aware Contrastive-Learning-Based GCN for Recommendation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3226369
Abstract: Graph convolutional networks (GCNs) have shown great potential in recommender systems. GCN models contain multiple layers of graph convolutions to exploit signals from higher-order neighbors. In each graph convolution, the embedding of a user or… read more here.
Keywords: based gcn; interest aware; contrastive learning; aware contrastive ... See more keywords
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3163806
Abstract: A fundamental and challenging problem in deep learning is catastrophic forgetting, the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely… read more here.
Keywords: contrastive distillation; distillation; uncertainty aware; aware contrastive ... See more keywords
Towards Deviation-Robust Agent Navigation Via Perturbation-Aware Contrastive Learning.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.1109/tpami.2023.3273594
Abstract: Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN agents are trained typically under disturbance-free environments and may easily fail… read more here.
Keywords: deviation; navigation; perturbation aware; contrastive learning ... See more keywords