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 1741–1750 of 4891 papers

TitleStatusHype
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence ModelingCode2
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning—0
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation—0
GateAttentionPose: Enhancing Pose Estimation with Agent Attention and Improved Gated Convolutions—0
Mamba for Scalable and Efficient Personalized Recommendations—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods—0
The Weak Form Is Stronger Than You Think—0
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified