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 3531–3540 of 4891 papers

TitleStatusHype
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Optimistic Optimization of Gaussian Process Samples—0
Latent Similarity Identifies Important Functional Connections for Phenotype PredictionCode0
A Consistent ICM-based χ^2 Specification Test—0
Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimationCode0
Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints—0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—0
Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors—0
Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles—0
Towards Efficient Capsule NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified