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 2321–2330 of 4891 papers

TitleStatusHype
The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection—0
Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units—0
The Role of Extended Horizon Methodology in Renewable-Dense Grids With Inter-Day Long-Duration Energy Storage—0
Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language ModelsCode0
Machine learning-based system reliability analysis with Gaussian Process Regression—0
Is Mamba Effective for Time Series Forecasting?Code3
A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification—0
EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image RegistrationCode0
SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera ImagesCode1
Hierarchical Provision of Distribution Grid Flexibility with Online Feedback Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified