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

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Showing 3401–3410 of 4891 papers

TitleStatusHype
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear ProgrammingCode0
Rewarded meta-pruning: Meta Learning with Rewards for Channel PruningCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response—0
Spatially Covariant Lesion Segmentation—0
Logic programming for deliberative robotic task planning—0
Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction—0
An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Topologically Regularized Data EmbeddingsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified