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 651–660 of 4891 papers

TitleStatusHype
Fast Point TransformerCode1
Fully Attentional Network for Semantic SegmentationCode1
MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and ClassificationCode1
Sparse DETR: Efficient End-to-End Object Detection with Learnable SparsityCode1
Adaptive Fourier Neural Operators: Efficient Token Mixers for TransformersCode1
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender SystemsCode1
Gaussian Kernel Mixture Network for Single Image Defocus DeblurringCode1
SE(3) Equivariant Graph Neural Networks with Complete Local FramesCode1
Exploiting Redundancy: Separable Group Convolutional Networks on Lie GroupsCode1
Neural Flows: Efficient Alternative to Neural ODEsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified