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 481–490 of 4891 papers

TitleStatusHype
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point CloudsCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety FilterCode1
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 SegmentationCode1
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
Model Editing by Standard Fine-TuningCode1
DiGRAF: Diffeomorphic Graph-Adaptive Activation FunctionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified