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 3791–3800 of 4891 papers

TitleStatusHype
Adaptive Focus for Efficient Video RecognitionCode1
Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization—0
Spectral Machine Learning for Pancreatic Mass Imaging Classification—0
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction—0
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning—0
ELF-VC: Efficient Learned Flexible-Rate Video Coding—0
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-RepsCode1
BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data—0
Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access—0
Efficient training of physics-informed neural networks via importance sampling—0
Show:102550
← PrevPage 380 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified