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 1931–1940 of 4891 papers

TitleStatusHype
Extreme Value k-means Clustering—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications—0
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition—0
Few-Shot Data-Driven Algorithms for Low Rank Approximation—0
Extracting man-made objects from remote sensing images via fast level set evolutions—0
Extracting and Learning a Dependency-Enhanced Type Lexicon for Dutch—0
Fighting Fire with Fire (F3): A Training-free and Efficient Visual Adversarial Example Purification Method in LVLMs—0
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion—0
Extending Machine Learning-Based Early Sepsis Detection to Different Demographics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified