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Found 13 papers, 4 papers with code
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent.
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures
Additionally, for the privacy security of FLGNN, this paper designs membership inference attack experiments and differential privacy defense experiments.
Target Recognition Algorithm for Monitoring Images in Electric Power Construction Process
To enhance precision and comprehensiveness in identifying targets in electric power construction monitoring video, a novel target recognition algorithm utilizing infrared imaging is explored.
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration.
Clustering-based Multicast Scheme for UAV Networks
When an unmanned aerial vehicle (UAV) network is utilized as an aerial small base station (BS), like a relay deployed far away from macro BSs, existing multicast methods based on acknowledgement (ACK) feedback and retransmissions may encounter severe delay and signaling overhead due to hostile wireless environments caused by a long-distance propagation and numerous UAVs.
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words.
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence.
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence.
HyperStream: a Workflow Engine for Streaming Data
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities.
Distribution Calibration for Regression
We are concerned with obtaining well-calibrated output distributions from regression models.
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach
To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics.
Non-Parametric Calibration of Probabilistic Regression
The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable.
Reconstruction of network structures from repeating spike patterns in simulated bursting dynamics
Repeating patterns of spike sequences from a neuronal network have been proposed to be useful in the reconstruction of the network topology.
Neurons and Cognition
