The bi-channel paradigm separates database networking into a high-performance UDP data path and a TCP control path to reduce kernel overhead while preserving reliability on fast cloud networks.
Research on overfitting of deep learning
11 Pith papers cite this work. Polarity classification is still indexing.
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Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
GenSP learns a continuous neural deformation model from sphere coordinates and latent codes to produce consistent spherical parameterizations for genus-0 shapes.
Implicit neural fields enable joint optimization of manufacturing layers and toolpaths with explicit collision avoidance in a single differentiable pipeline for multi-axis processes.
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
Learned cardiac motion priors for INRs, particularly meta-learning, improve early adaptation performance and tracking accuracy on UK Biobank tagged CMR images compared to random initialization.
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
A review comparing three consciousness theories and outlining an adversarial collaboration to test their predictions through integrated multi-site experiments.
Z-Score Filtered SAM retains only high absolute Z-score gradient components per layer during the ascent step and reports higher test accuracy than standard SAM on CIFAR and Tiny-ImageNet benchmarks.
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GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models
GenSP learns a continuous neural deformation model from sphere coordinates and latent codes to produce consistent spherical parameterizations for genus-0 shapes.
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Learning Cardiac Motion Priors for Implicit Neural Representations
Learned cardiac motion priors for INRs, particularly meta-learning, improve early adaptation performance and tracking accuracy on UK Biobank tagged CMR images compared to random initialization.