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 4001–4010 of 4891 papers

TitleStatusHype
Data-Centric Learning Framework for Real-Time Detection of Aiming Beam in Fluorescence Lifetime Imaging Guided Surgery—0
Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm—0
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment—0
Data denoising with self consistency, variance maximization, and the Kantorovich dominance—0
Data-driven approaches to inverse problems—0
Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems—0
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response—0
Data-driven HRF estimation for encoding and decoding models—0
Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference—0
Data-driven Modeling of Linearizable Power Flow for Large-scale Grid Topology Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified