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 1621–1630 of 4891 papers

TitleStatusHype
Exploring the effects of robotic design on learning and neural controlCode0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
Coping With Simulators That Don't Always ReturnCode0
Scalable Verification of Quantized Neural Networks (Technical Report)Code0
Ensemble Prediction via Covariate-dependent StackingCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
Enhancing Character-Level Understanding in LLMs through Token Internal Structure LearningCode0
Energy and polarization based on-line interference mitigation in radio interferometryCode0
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based ModelsCode0
Are Gradients on Graph Structure Reliable in Gray-box Attacks?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified