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 2271–2280 of 4891 papers

TitleStatusHype
An Efficient Procedure for Computing Bayesian Network Structure Learning—0
Efficient Second-Order Neural Network Optimization via Adaptive Trust Region Methods—0
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning—0
Efficient Search-Based Weighted Model Integration—0
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking—0
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection—0
An Efficient Privacy-aware Split Learning Framework for Satellite Communications—0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
Efficient safe learning for controller tuning with experimental validation—0
Efficient Representations for High-Cardinality Categorical Variables in Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified