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 3821–3830 of 4891 papers

TitleStatusHype
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical ZoomCode0
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech—0
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliersCode0
Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere—0
Checkerboard Context Model for Efficient Learned Image CompressionCode1
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples—0
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and RegularizationCode1
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning—0
Backup Plan Constrained Model Predictive Control—0
Combining distribution-based neural networks to predict weather forecast probabilitiesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified