Long short-term memory networks for earthquake detection in Venezuelan regions

Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Boconó and La Victoria - San Sebastián faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.Peer ReviewedPostprint (author's final draft

Variance Loss in Variational Autoencoders

In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced from an extensive experimentation with different network architectures and datasets: the variance of generated data is significantly lower than that of training data. Since generative models are usually evaluated with metrics such as the Frechet Inception Distance (FID) that compare the distributions of (features of) real versus generated images, the variance loss typically results in degraded scores. This problem is particularly relevant in a two stage setting, where we use a second VAE to sample in the latent space of the first VAE. The minor variance creates a mismatch between the actual distribution of latent variables and those generated by the second VAE, that hinders the beneficial effects of the second stage. Renormalizing the output of the second VAE towards the expected normal spherical distribution, we obtain a sudden burst in the quality of generated samples, as also testified in terms of FID.Comment: Article accepted at the Sixth International Conference on Machine Learning, Optimization, and Data Science. July 19-23, 2020 - Certosa di Pontignano, Siena, Ital

Comparing the latent space of generative models

Different encodings of datapoints in the latent space of latent-vector generative models may result in more or less effective and disentangled characterizations of the different explanatory factors of variation behind the data. Many works have been recently devoted to the explorationof the latent space of specific models, mostly focused on the study of how features are disentangled and of how trajectories producing desired alterations of data in the visible space can be found. In this work we address the more general problem of comparing the latent spaces of different models, looking for transformations between them. We confined the investigation to the familiar and largely investigated case of generative models for the data manifold of human faces. The surprising, preliminary result reported in this article is that (provided models have not been taught or explicitly conceived to act differently) a simple linear mapping is enough to pass from a latent space to another while preserving most of the information

Pareto multi-task deep learning

Neuroevolution has been used to train Deep Neural Networks on reinforcement learning problems. A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for tackling both multi-task and multi-objective problems. In this method prior knowledge on the tasks is used to explicitly define multiple utility functions, which are optimized simultaneously. Experimental results on some Atari 2600 games, a challenging testbed for deep reinforcement learning algorithms, show that a single neural network with a single set of parameters can outperform previous state of the art techniques. In addition to the standard analysis, all results are also evaluated using the Hypervolume indicator and the Kullback-Leibler divergence to get better insights on the underlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks

Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoders

In the loss function of Variational Autoencoders there is a well known tension between two components: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space. Correctly balancing these two components is a delicate issue, easily resulting in poor generative behaviours. In a recent work, Dai and Wipf obtained a sensible improvement by allowing the network to learn the balancing factor during training, according to a suitable loss function. In this article, we show that learning can be replaced by a simple deterministic computation, helping to understand the underlying mechanism, and resulting in a faster and more accurate behaviour. On typical datasets such as Cifar and Celeba, our technique sensibly outperforms all previous VAE architectures

Generazione di attributi facciali mediante Feature-wise Linear Modulation

L’aspetto dell’apprendimento automatico su cui si sta lavorando di più, negli ultimi anni, è quello della generazione di dati, come ad esempio suoni, testi e immagini. Un aspetto interessante nel campo della generazione è la possibilità di condizionare il modo in cui la rete neurale genera nuovi dati. Recentemente è stata introdotta la tecnica del Feature-wise Linear Modulation, abbreviato “FiLM”, usata per influenzare in modo adattivo l’output di una rete neurale basandosi su un input arbitrario, applicando una trasformazione affine sulle features intermedie della rete. Lo scopo dell’elaborato è mostrare l’integrazione di livelli FiLM all'interno di un modello Variational Autoencoder (VAE). Il modello così ottenuto verrà analizzato per le sue capacità di ricostruzione dell’input e di generazione di nuovi volti umani, sulla base di specifici attributi. Il modello verrà allenato sui volti presenti nel dataset CelebA e ne verrà valutata la capacità di ricostruzione e generazione attraverso la metrica della Fréchet Inception Distance (FID). Inoltre verrà condotto un piccolo esperimento per valutare la capacità del FID di discriminare alcuni attributi

Machine Learning Algorithm for the Scansion of Old Saxon Poetry

Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses