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 3041–3050 of 4891 papers

TitleStatusHype
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels—0
Detection and Classification of Glioblastoma Brain Tumor—0
Improving Autoregressive NLP Tasks via Modular Linearized Attention—0
Reconfigurable Intelligent Surface-Enabled Gridless DoA Estimation System for NLoS Scenarios—0
Comparative Study of MPPT and Parameter Estimation of PV cells—0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain—0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Learning in latent spaces improves the predictive accuracy of deep neural operatorsCode1
A Machine Learning-Enhanced Benders Decomposition Approach to Solve the Transmission Expansion Planning Problem under Uncertainty—0
Model Predictive Control with Self-supervised Representation LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified