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 361–370 of 4891 papers

TitleStatusHype
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Sparse Ellipsoidal Radial Basis Function Network for Point Cloud Surface RepresentationCode0
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
Beyond the model: Key differentiators in large language models and multi-agent services—0
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-ResolutionCode1
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs—0
Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification—0
GauS-SLAM: Dense RGB-D SLAM with Gaussian Surfels—0
Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified