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 1791–1800 of 4891 papers

TitleStatusHype
SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians—0
Different thresholding methods on Nearest Shrunken Centroid algorithm—0
Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems—0
DeepF-fNet: a physics-informed neural network for vibration isolation optimization—0
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning—0
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems—0
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching—0
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
Frames and vertex-frequency representations in graph fractional Fourier domain—0
Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified