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 1171–1180 of 4891 papers

TitleStatusHype
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning—0
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems—0
Frames and vertex-frequency representations in graph fractional Fourier domain—0
Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset—0
TeLU Activation Function for Fast and Stable Deep LearningCode1
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
Bottom-up robust modeling for the foraging behavior of Physarum polycephalum—0
Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedback—0
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement—0
Revisiting PCA for time series reduction in temporal dimensionCode7
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified