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Found 20 papers, 9 papers with code
Modularity based linkage model for neuroevolution
Crossover between neural networks is considered disruptive due to the strong functional dependency between connection weights.
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
Results show the system is able to effectively explore the trade-off between policy performance and complexity, and learn interpretable, high-performing policies that use as few rules as possible.
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS
Learning Classifier Systems (LCSs) are evolutionary machine learning systems that can be categorised as eXplainable AI (XAI) due to their rule-based nature.
Explainable Artificial Intelligence (XAI)
reinforcement-learning
+1
Modularity in NEAT Reinforcement Learning Networks
Modularity is essential to many well-performing structured systems, as it is a useful means of managing complexity [8].
Approximate discounting-free policy evaluation from transient and recurrent states
We therefore propose a system of approximators for the bias (specifically, its relative value) from transient and recurrent states.
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK).
From Zero-Shot Machine Learning to Zero-Day Attack Detection
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase.
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks
In an analysis of related works, it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction (FR) and Machine Learning (ML) techniques on NIDS datasets.
Examining average and discounted reward optimality criteria in reinforcement learning
In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality criterion is fundamentally important.
A nearly Blackwell-optimal policy gradient method
In this work, we develop a policy gradient method that optimizes the gain, then the bias (which indicates the transient performance and is important to capably select from policies with equal gain).
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets
Our results show that the two real-world datasets are quite similar among themselves in regards to most of the considered statistical features.
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Reinforcement learning is important part of artificial intelligence.
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning
Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments.
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions.
Richer priors for infinitely wide multi-layer perceptrons
The model resulting from partially exchangeable priors is a GP, with an additional level of inference in the sense that the prior and posterior predictive distributions require marginalisation over hyperparameters.
Reversible Jump Probabilistic Programming
In this paper we present a method for automatically deriving a Reversible Jump Markov chain Monte Carlo sampler from probabilistic programs that specify the target and proposal distributions.
Exchangeability and Kernel Invariance in Trained MLPs
In the analysis of machine learning models, it is often convenient to assume that the parameters are IID.
Invariance of Weight Distributions in Rectified MLPs
An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network.
A Stochastic Process Model of Classical Search
In this paper, we formalize the process of classical search as a metalevel decision problem, the Abstract Search MDP.
