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Neural networks : the official journal of the International Neural Network Society
Feb 22, 2025
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data samples are often decentralized and Non-Independently Identically Dist...
Neural networks : the official journal of the International Neural Network Society
Jan 25, 2025
Low-Rank Representation (LRR) methods integrate low-rank constraints and projection operators to model the mapping from the sample space to low-dimensional manifolds. Nonetheless, existing approaches typically apply Euclidean algorithms directly to m...
Neural networks : the official journal of the International Neural Network Society
Jan 23, 2025
Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing metho...
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
The current few-shot relational triple extraction (FS-RTE) techniques, which rely on prototype networks, have made significant progress. Nevertheless, the scarcity of data in the support set results in both intra-class and inter-class gaps in FS-RTE....
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
This paper proposes a new continual learning method with Bayesian Compression for Shared and Private Latent Representations (BCSPLR), which learns a compact model structure while preserving the accuracy. In Shared and Private Latent Representations (...
Neural networks : the official journal of the International Neural Network Society
Feb 26, 2025
In feed-forward neural networks, dataset-free weight-initialization methods such as LeCun, Xavier (or Glorot), and He initializations have been developed. These methods randomly determine the initial values of weight parameters based on specific dist...
Neural networks : the official journal of the International Neural Network Society
Mar 1, 2025
Learning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opportunities to develop representations that are consistent with the inf...
Neural networks : the official journal of the International Neural Network Society
Mar 1, 2025
The marriage of deep neural network (DNN) and secure 2-party computation (2PC) enables private inference (PI) on the encrypted client-side data and server-side models with both privacy and accuracy guarantees, coming at the cost of orders of magnitud...
Neural networks : the official journal of the International Neural Network Society
Mar 3, 2025
The objective of multi-label image classification (MLIC) task is to simultaneously identify multiple objects present in an image. Several researchers directly flatten 2D feature maps into 1D grid feature sequences, and utilize Transformer encoder to ...