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 2811–2820 of 4891 papers

TitleStatusHype
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel—0
SGMM: Stochastic Approximation to Generalized Method of Moments—0
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems—0
QKSAN: A Quantum Kernel Self-Attention Network—0
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsCode2
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models—0
Probabilistic load forecasting with Reservoir Computing—0
VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition—0
Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-AttentionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified