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Found 10 papers, 2 papers with code
Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach
To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques.
Yelp Reviews and Food Types: A Comparative Analysis of Ratings, Sentiments, and Topics
This study examines the relationship between Yelp reviews and food types, investigating how ratings, sentiments, and topics vary across different types of food.
Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach
This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture.
Exploring the Emotional and Mental Well-Being of Individuals with Long COVID Through Twitter Analysis
The COVID-19 pandemic has led to the emergence of Long COVID, a cluster of symptoms that persist after infection.
Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach
However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs.
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization
This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature.
Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach
In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user.
CLPsych2019 Shared Task: Predicting Suicide Risk Level from Reddit Posts on Multiple Forums
The NB model had the best performance in two additional binary-classification tasks, i. e., no risk vs. flagged risk (any risk level other than no risk) with F1 score 0. 836 and no or low risk vs. urgent risk (moderate or severe risk) with F1 score 0. 736.
Correlated Anomaly Detection from Large Streaming Data
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Previous research has shown that Genetic Algorithms (GA) typically are a good choice to solve this problem when the solution space is relatively small.
