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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2301–2310 of 4891 papers

TitleStatusHype
Medical Image Data Provenance for Medical Cyber-Physical System—0
Spectral Motion Alignment for Video Motion Transfer using Diffusion Models—0
Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets—0
Considerations in the use of ML interaction potentials for free energy calculationsCode0
Sparse Implementation of Versatile Graph-Informed LayersCode0
vid-TLDR: Training Free Token merging for Light-weight Video TransformerCode2
Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition—0
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection—0
Community detection by spectral methods in multi-layer networks—0
Enhancing Vision-Language Few-Shot Adaptation with Negative LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified