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Found 9 papers, 4 papers with code
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
The advent of Industry 4. 0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence.
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data.
Sample-Level Multi-View Graph Clustering
Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data.
MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images
In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique. Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly. Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies. By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications.
Partial Differential Equations Meet Deep Neural Networks: A Survey
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling.
Variational Graph Generator for Multi-View Graph Clustering
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
Multi-graph Fusion for Multi-view Spectral Clustering
Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.
Multiple Partitions Aligned Clustering
Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie.
