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 3341–3350 of 4891 papers

TitleStatusHype
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
Interpretable Ensembles of Hyper-Rectangles as Base ModelsCode0
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces—0
Importance Filtering with Risk Models for Complex Driving Situations—0
Gradient-Descent Based Optimization of Constant Envelope OFDM Waveforms—0
BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving—0
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian OptimizationCode0
Scattering and Gathering for Spatially Varying Blurs—0
A pseudo-likelihood approach to community detection in weighted networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified