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 2461–2470 of 4891 papers

TitleStatusHype
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State SpacesCode3
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques—0
Speeding up and reducing memory usage for scientific machine learning via mixed precisionCode1
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models—0
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization—0
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier SeriesCode0
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks—0
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP—0
Statistical Significance of Feature Importance RankingsCode0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Show:102550
← PrevPage 247 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified