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Found 9 papers, 0 papers with code
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model.
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC).
Tracking changes using Kullback-Leibler divergence for the continual learning
Recently, continual learning has received a lot of attention.
Prediction of Facebook Post Metrics using Machine Learning
In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook.
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
Then, according to our empirical results, we can see that this border seems to be closer to the sufficient conditions than to the necessary conditions of the ESP.
A Tutorial about Random Neural Networks in Supervised Learning
Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network.
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
The model is applied using public genomic data.
An Empirical Study of the L2-Boost technique with Echo State Networks
A particular case of Recurrent Neural Network (RNN) was introduced at the beginning of the 2000s under the name of Echo State Networks (ESNs).
An Experimental Analysis of the Echo State Network Initialization Using the Particle Swarm Optimization
Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model.
