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 2861–2870 of 4891 papers

TitleStatusHype
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks—0
Unlocking the Potential of Similarity Matching: Scalability, Supervision and Pre-training—0
A Real-Time Robust Ecological-Adaptive Cruise Control Strategy for Battery Electric Vehicles—0
Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations—0
WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique—0
VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture DesignCode2
An Unforgeable Publicly Verifiable Watermark for Large Language ModelsCode2
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
Show:102550
← PrevPage 287 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified