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 1131–1140 of 4891 papers

TitleStatusHype
Do global forecasting models require frequent retraining?—0
Generative QoE Modeling: A Lightweight Approach for Telecom Networks—0
Kernel Density Machines—0
Data-driven operator learning for energy-efficient building control—0
From Precision to Perception: User-Centred Evaluation of Keyword Extraction Algorithms for Internet-Scale Contextual Advertising—0
Make Both Ends Meet: A Synergistic Optimization Infrared Small Target Detection with Streamlined Computational Overhead—0
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification—0
GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest—0
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR—0
Cognitive maps are generative programs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified