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 2171–2180 of 4891 papers

TitleStatusHype
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning—0
GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB ImagesCode1
BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global MapsCode3
ReActXGB: A Hybrid Binary Convolutional Neural Network Architecture for Improved Performance and Computational Efficiency—0
Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA—0
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image SegmentationCode3
Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier DesignsCode1
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants—0
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression ToolkitCode4
Outlier-robust Kalman Filtering through Generalised BayesCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified