Mechanical conscience is proposed as a trajectory-level regulatory filter for AI policies that reduces cumulative deviation from admissible regions, with claimed theoretical properties and extension to multi-agent settings.
The emergence of edge computing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.
citing papers explorer
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Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence
Mechanical conscience is proposed as a trajectory-level regulatory filter for AI policies that reduces cumulative deviation from admissible regions, with claimed theoretical properties and extension to multi-agent settings.
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A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
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A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
A trust-aware federated hybrid intrusion detection framework using random forest, decision tree, and linear SVM is proposed for intelligent transport systems with edge computing.