Search Results for author:
Found 7 papers, 1 papers with code
Stability and Generalization of Hypergraph Collaborative Networks
The analysis sheds light on the design of hypergraph filters in collaborative networks, for instance, how the data and hypergraph filters should be scaled to achieve uniform stability of the learning process.
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes
In this paper, we propose an intention neural process model (INP) for user cold-start recommendation (i. e., user with very few historical interactions), a novel extension of the neural stochastic process family using a general meta learning strategy with intrinsic and extrinsic intention learning for robust user preference learning.
A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing
The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications.
Tucker Decomposition Network: Expressive Power and Comparison
Deep neural networks have achieved a great success in solving many machine learning and computer vision problems.
Deep Plug-and-play Prior for Low-rank Tensor Completion
By integrating deterministic low-rankness prior to the data-driven deep prior, we suggest a novel regularized tensor completion model for multi-dimensional image completion.
A Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition
Recently, there has been a lot of research into tensor singular value decomposition (t-SVD) by using discrete Fourier transform (DFT) matrix.
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
