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 471–480 of 4891 papers

TitleStatusHype
Efficient Aggregated Kernel Tests using Incomplete U-statisticsCode1
SE(3) Equivariant Graph Neural Networks with Complete Local FramesCode1
Flash Window Attention: speedup the attention computation for Swin TransformerCode1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelsCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
Mask Image WatermarkingCode1
MatIR: A Hybrid Mamba-Transformer Image Restoration ModelCode1
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model TrainingCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified