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Found 6 papers, 2 papers with code
Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks
Our analysis indicates that the transformed low-rank parameterization can promisingly enhance robust generalization for t-NNs.
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
Then, we introduce a novel structure-mixing knowledge distillation strategy to enhance the learning ability of MLPs for structure information.
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications
In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced.
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification
In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention.
Tensor-Train Parameterization for Ultra Dimensionality Reduction
Thus, we propose a tensor-train parameterization for ultra dimensionality reduction (TTPUDR) in which the traditional LPP mapping is tensorized in terms of tensor-trains and the LPP objective is replaced with the Frobenius norm to increase the robustness of the model.
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis
In this paper, we propose a new variant of tensorial neural networks which directly take tensorial time series data as inputs.
