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 3031–3040 of 4891 papers

TitleStatusHype
Towards Compute-Optimal Transfer Learning—0
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer EvaluationsCode0
Centrality-Based Node Feature Augmentation for Robust Network Alignment—0
Optimal Design of Neural Network Structure for Power System Frequency Security Constraints—0
A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles—0
Gradient-Descent Based Optimization of Multi-Tone Sinusoidal Frequency Modulated Waveforms—0
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style TransfersCode1
SSN: Stockwell Scattering Network for SAR Image Change Detection—0
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster ImagesCode0
OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified