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 1611–1620 of 4891 papers

TitleStatusHype
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
Cortical surface registration using unsupervised learningCode0
Estimating Time-Varying Graphical ModelsCode0
ETC-NLG: End-to-end Topic-Conditioned Natural Language GenerationCode0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingCode0
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series ClassificationCode0
Coping With Simulators That Don't Always ReturnCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified