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 3661–3670 of 4891 papers

TitleStatusHype
Asymptotic Linearity of Consumption Functions and Computational Efficiency—0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization—0
Asynchronous Perception-Action-Communication with Graph Neural Networks—0
Comparing large language models and human programmers for generating programming code—0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
ATC-Based Scenario Decomposition Algorithm for Optimal Power Flow of Distribution Networks Considering High Photovoltaic Penetration—0
A Technical Study into Small Reasoning Language Models—0
A Temporal-oriented Broadcast ResNet for COVID-19 Detection—0
A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified