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 581–590 of 4891 papers

TitleStatusHype
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated UncertaintyCode1
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and ExplainabilityCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot SystemsCode1
Spatial-Spectral Transformer for Hyperspectral Image DenoisingCode1
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural NetworksCode1
Statistical Inference for Coadded Astronomical ImagesCode1
Model Predictive Control for Signal Temporal Logic Specifications with Time Interval DecompositionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified