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 2621–2630 of 4891 papers

TitleStatusHype
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning—0
Autonomous Sparse Mean-CVaR Portfolio OptimizationCode0
Ground-based image deconvolution with Swin Transformer UNet—0
A Low-rank Projected Proximal Gradient Method for Spectral Compressed Sensing—0
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
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants—0
D-CODE: Data Colony Optimization for Dynamic Network Efficiency—0
SUTRA: Scalable Multilingual Language Model Architecture—0
High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural Operators—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified