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 3491–3500 of 4891 papers

TitleStatusHype
Distributionally Robust Model Predictive Control with Total Variation Distance—0
DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic Layouts—0
Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning—0
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC—0
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM ImagesCode1
Neural Theorem Provers Delineating Search Area Using RNN—0
Efficient universal shuffle attack for visual object tracking—0
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
Integrating Dependency Tree Into Self-attention for Sentence Representation—0
Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data—0
Show:102550
← PrevPage 350 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified