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 4151–4160 of 4891 papers

TitleStatusHype
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking—0
Semantic Segmentation of Underwater Imagery: Dataset and BenchmarkCode1
MUXConv: Information Multiplexing in Convolutional Neural NetworksCode1
Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model—0
Deep reinforcement learning for large-scale epidemic controlCode1
Coping With Simulators That Don't Always ReturnCode0
Multi-target regression via output space quantization—0
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems—0
Show:102550
← PrevPage 416 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified