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 4126–4150 of 4891 papers

TitleStatusHype
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks—0
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network—0
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks—0
Acoustic Source Localization with the Angular Spectrum Approach in Continuously Stratified Media—0
Statistical Mechanical Analysis of Neural Network PruningCode0
Similarity Search for Efficient Active Learning and Search of Rare Concepts—0
Variational Autoencoding of PDE Inverse Problems—0
Localization Uncertainty Estimation for Anchor-Free Object Detection—0
Differential Privacy of Hierarchical Census Data: An Optimization Approach—0
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator—0
Recurrent Relational Memory Network for Unsupervised Image Captioning—0
Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks—0
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control—0
Gradient-EM Bayesian Meta-learning—0
A deep convolutional neural network model for rapid prediction of fluvial flood inundation—0
Neural Architecture Optimization with Graph VAE—0
The Nyström method for convex loss functions—0
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated FeaturesCode0
An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing—0
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron—0
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
AlgebraNetsCode0
Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified