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 621–630 of 4891 papers

TitleStatusHype
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
itKD: Interchange Transfer-based Knowledge Distillation for 3D Object DetectionCode1
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connectionsCode1
Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class ClassificationCode1
Transkimmer: Transformer Learns to Layer-wise SkimCode1
View Synthesis with Sculpted Neural PointsCode1
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural NetworksCode1
SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible ImagesCode1
Dynamic Multimodal FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified