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 1161–1170 of 4891 papers

TitleStatusHype
Learning Weather Models from Data with WSINDy—0
HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer SegmentationCode1
AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing GraphsCode0
Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1 Knapsack Problems—0
NeuroSleepNet: A Multi-Head Self-Attention Based Automatic Sleep Scoring Scheme with Spatial and Multi-Scale Temporal Representation Learning—0
Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models—0
Different thresholding methods on Nearest Shrunken Centroid algorithm—0
SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians—0
DeepF-fNet: a physics-informed neural network for vibration isolation optimization—0
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified