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 3291–3300 of 4891 papers

TitleStatusHype
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Learning battery model parameter dynamics from data with recursive Gaussian process regression—0
Centrality-Based Node Feature Augmentation for Robust Network Alignment—0
Towards Compute-Optimal Transfer Learning—0
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer EvaluationsCode0
A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles—0
Optimal Design of Neural Network Structure for Power System Frequency Security Constraints—0
Gradient-Descent Based Optimization of Multi-Tone Sinusoidal Frequency Modulated Waveforms—0
SSN: Stockwell Scattering Network for SAR Image Change Detection—0
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified