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 2751–2760 of 4891 papers

TitleStatusHype
HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion—0
A parsimonious, computationally efficient machine learning method for spatial regression—0
An Enhanced Low-Resolution Image Recognition Method for Traffic Environments—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
Partial Transport for Point-Cloud Registration—0
Balancing Computational Efficiency and Forecast Error in Machine Learning-based Time-Series Forecasting: Insights from Live Experiments on Meteorological Nowcasting—0
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single CameraCode0
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified