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 2091–2100 of 4891 papers

TitleStatusHype
Change Detection from Synthetic Aperture Radar Images via Dual Path Denoising Network—0
Ergodic Inference: Accelerate Convergence by Optimisation—0
ERes2NetV2: Boosting Short-Duration Speaker Verification Performance with Computational Efficiency—0
Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection—0
An FPGA-Based Accelerator Enabling Efficient Support for CNNs with Arbitrary Kernel Sizes—0
Equivariant Transformers for Neural Network based Molecular Potentials—0
CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs—0
Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis—0
Show:102550
← PrevPage 210 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified