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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2061–2070 of 4891 papers

TitleStatusHype
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration—0
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC—0
3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy—0
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping—0
Evaluation of adaptive sampling methods in scenario generation for virtual safety impact assessment of pre-crash safety systems—0
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation—0
Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors—0
CIMON: Towards High-quality Hash Codes—0
An Improved Finite Element Modeling Method for Triply Periodic Minimal Surface Structures Based on Element Size and Minimum Jacobian—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified