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 2361–2370 of 4891 papers

TitleStatusHype
D4C Glove-train: Solving the RPM and Bongard-logo Problem by Circumscribing and Building Distribution for Concepts—0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation—0
Single-level Robust Bidding of Renewable-only Virtual Power Plant in Energy and Ancillary Service Markets for Worst-case Profit—0
ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic SeizuresCode1
A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral imagesCode0
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment—0
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical SystemsCode0
Vanilla Transformers are Transfer Capability Teachers—0
Revisiting Learning-based Video Motion Magnification for Real-time Processing—0
Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified