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 4191–4200 of 4891 papers

TitleStatusHype
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems—0
A High-Performance Object Proposals based on Horizontal High Frequency Signal—0
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching—0
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles—0
Energy-efficient and Robust Cumulative Training with Net2Net Transformation—0
Learning Directly from Grammar Compressed TextCode0
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation—0
Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models—0
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified