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 4001–4010 of 4891 papers

TitleStatusHype
Episodic Memory for Learning Subjective-Timescale Models—0
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks—0
Self-grouping Convolutional Neural NetworksCode1
Multi-Relational Embedding for Knowledge Graph Representation and AnalysisCode1
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time—0
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World—0
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes—0
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified