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Found 4 papers, 4 papers with code
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources.
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the unsupervised setting in most GAD studies with a fully unlabeled graph.
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
In this work, we explore this property to introduce a novel unsupervised anomaly scoring measure for GAD, local node affinity, that assigns a larger anomaly score to nodes that are less affiliated with their neighbors, with the affinity defined as similarity on node attributes/representations.
Deep Learning for Hate Speech Detection: A Comparative Study
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media.
