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 3221–3230 of 4891 papers

TitleStatusHype
whittlehurst: A Python package implementing Whittle's likelihood estimation of the Hurst exponent—0
Whole-brain substitute CT generation using Markov random field mixture models—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Why Size Matters: Feature Coding as Nystrom Sampling—0
Will Bilevel Optimizers Benefit from Loops—0
WITCHcraft: Efficient PGD attacks with random step size—0
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care—0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference—0
XPose: eXplainable Human Pose Estimation—0
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified