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 1631–1640 of 4891 papers

TitleStatusHype
Are Gradients on Graph Structure Reliable in Gray-box Attacks?Code0
Energy and polarization based on-line interference mitigation in radio interferometryCode0
Converting Transformers into DGNNs FormCode0
Controlling Participation in Federated Learning with FeedbackCode0
End-to-End Deep Learning for Structural Brain Imaging: A Unified FrameworkCode0
Seeded Poisson Factorization: Leveraging domain knowledge to fit topic modelsCode0
Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound ImagesCode0
SegRet: An Efficient Design for Semantic Segmentation with Retentive NetworkCode0
Continuous vs. Discrete Optimization of Deep Neural NetworksCode0
End-to-end reconstruction meets data-driven regularization for inverse problemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified