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 1871–1880 of 4891 papers

TitleStatusHype
Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box ModelsCode1
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens—0
Retrieval Augmentation via User Interest Clustering—0
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8—0
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling—0
RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution NetworksCode2
Doubly Stochastic Adaptive Neighbors Clustering via the Marcus Mapping—0
Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network—0
Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNsCode0
BOTS-LM: Training Large Language Models for Setswana—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified