SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 3051–3060 of 4891 papers

TitleStatusHype
Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction—0
DynamicDet: A Unified Dynamic Architecture for Object DetectionCode1
Gradient-Free Textual Inversion—0
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
State estimation of a carbon capture process through POD model reduction and neural network approximation—0
Scale-Space Hypernetworks for Efficient Biomedical Imaging—0
Machine learning for structure-property relationships: Scalability and limitations—0
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty—0
A Framework for Combustion Chemistry Acceleration with DeepONets—0
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified