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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 511–520 of 4891 papers

TitleStatusHype
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
DiGRAF: Diffeomorphic Graph-Adaptive Activation FunctionCode1
Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationCode1
Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety FilterCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable ConvolutionCode1
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
Show:102550
← PrevPage 52 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified