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 1201–1210 of 4891 papers

TitleStatusHype
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative HypotensionCode0
Kernel Heterogeneity Improves Sparseness of Natural Images RepresentationsCode0
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networksCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
A Low-complexity Structured Neural Network to Realize States of Dynamical SystemsCode0
AdamNODEs: When Neural ODE Meets Adaptive Moment EstimationCode0
Gaussian Mixture Reduction with Composite Transportation DivergenceCode0
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified