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 3371–3380 of 4891 papers

TitleStatusHype
Adaptive Sampling for Continuous Group Equivariant Neural Networks—0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables—0
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
A Non-Parametric Bootstrap for Spectral Clustering—0
A deep convolutional neural network model for rapid prediction of fluvial flood inundation—0
A Deeper Look at 3D Shape Classifiers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified