Technology
Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers
For example, images from two different satellites may both contain RGB channels, but the remaining channels can be different for each imaging source.
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations Qitian Wu
Transformers may have sufficient supervision for generalization.
UntrainedNeuralNetsforSnapshotCompressive Imaging: TheoryandAlgorithms
In this paper, we focus on SCI recovery algorithms that employ untrained neural networks (UNNs), such as deep image prior (DIP), to model source structure.
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In this study, we introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion yet facilitates end-to-end inference through joint probability.
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
We propose a versatile framework that combines DEQ with off-the-shelf machine learning models for predicting Hamiltonians.