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 611–620 of 4891 papers

TitleStatusHype
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsCode1
Corner Proposal Network for Anchor-free, Two-stage Object DetectionCode1
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud RegistrationCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast DescriptorCode1
Gracefully Filtering Backdoor Samples for Generative Large Language Models without RetrainingCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical AnalysisCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified