Man

Carlos Antonio Valle Vidal

Académico Jornada Completa

Universidad de Playa Ancha

Valparaíso, Chile

Líneas de Investigación


Machine Learning; Ensemble Learning; Data Mining, Artificial Neural Networks, Deep Learning. Aplicaciones de estos temas en Energía, Minería e Ingeniería Costera.

Educación

  •  Magister en Ciencias de la Ingeniería Informática, UNIVERSIDAD TECNICA FEDERICO SANTA MARIA. Chile, 2005
  •  Doctorado en Ingeniería Informática, UNIVERSIDAD TECNICA FEDERICO SANTA MARIA. Chile, 2014
  •  Ingeniero Civil Informático, UNIVERSIDAD TECNICA FEDERICO SANTA MARIA. Chile, 2005

Experiencia Académica

  •   Investigador Asociado Full Time

    UNIVERSIDAD TECNICA FEDERICO SANTA MARIA

    Valparaíso, Chile

    2014 - 2018

  •   Académico Asociado Full Time

    UNIVERSIDAD DE PLAYA ANCHA DE CIENCIAS DE LA EDUCACION

    Ingeniería

    Valparaíso, Chile

    2018 - A la fecha

Experiencia Profesional

  •   Profesor

    Universidad Técnica Federico Santa María

    Chile

    2003 - 2010

  •   Investigador Asociado Full Time

    Universidad Técnica Federico Santa María

    Valparaíso, Chile

    2014 - 2018

  •   Jefe de Carrera Ingeniería Civil Informatica

    Universidad Técnica Federico Santa María

    Chile

    2006 - 2006

  •   Académico Full Time

    Universidad de Playa Ancha

    Valparaíso, Chile

    2018 - A la fecha

Premios y Distinciones

  •   Best Paper Award

    CIARP 2018 (Universidad Autónoma de Madrid)

    España, 2018

    Mejor artículo en la conferencia CIARP 2018, Madrid España.


 

Article (25)

An Attention-Based Architecture for Hierarchical Classification With CNNs
Hybrid Algorithms for Energy Minimizing Vehicle Routing Problem: Integrating Clusterization and Ant Colony Optimization
Machine learning applications for urban photovoltaic potential estimation: A survey
Multi-step probabilistic forecasting model using deep learning parametrized distributions
Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks
Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets
Topic Models Ensembles for AD-HOC Information Retrieval
Addressing model uncertainty in probabilistic forecasting using Monte Carlo dropout
Automating Configuration of Convolutional Neural Network Hyperparameters Using Genetic Algorithm
Spikes and Nets (S&N): A New Fast, Parallel Computing, Point Process Software for Multineuronal Discharge and Connectivity Analysis
A Multi-Scale Model based on the Long Short-Term Memory for day ahead hourly wind speed forecasting
Boosting text clustering using topic selection
LocalBoost: A Parallelizable Approach to Boosting Classifiers
Long short-term memory networks based in echo state networks for wind speed forecasting
Multi-horizon scalable wind power forecast system
Neural networks for the reconstruction and separation of high energy particles in a preshower calorimeter
Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory
Improving the weighted distribution estimation for AdaBoost using a novel concurrent approach
Behavior analysis of neural network ensemble algorithm on a virtual machine cluster
Training regression ensembles by sequential target correction and resampling
Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks
Ensemble learning with local diversity
Local negative correlation with resampling
Moderated innovations in self-poised ensemble learning
Self-poised ensemble learning

ConferencePaper (8)

Boosting collaborative filters for drug-target interaction prediction
Forecasting Ozone Pollution using Recurrent Neural Nets and Multiple Quantile Regression
LSTM-based multi-scale model for wind speed forecasting
Probabilistic Forecasting Using Monte Carlo Dropout Neural Networks
Regularization for graph-based transfer learning text classification
Robust asymmetric Adaboost
Bagging with asymmetric costs for misclassified and correctly classified examples
Two bagging algorithms with coupled learners to encourage diversity

Proyecto (4)

Interpretable Deep Learning Models for Time Series Forecasting
Deep Recurrent Neural Networks for Multiscale Time Series Forecasting
MÉTODOS DE APRENDIZAJE AUTOMÁTICO (MACHINE LEARNING) APLICADOS A LA EXTRACCIÓN DE PATRONES SOBRE GRANDES STREAMS DE DATOS
Ensemble Learning Strategies for High-Dimensional and Non-Stationary Data
36
Carlos Valle

Académico Jornada Completa

Computación e Informática

Universidad de Playa Ancha

Valparaíso, Chile

14
Héctor Allende

Professor

Computer Science

UNIVERSIDAD TÉCNICA FEDERICO SANTA MARÍA

Valparaíso, Chile

5
JUAN NANCULEF

Full Time Professor

Universidad Técnica Federico Santa María

Santiago, Chile

2
Erick López

Profesor Asociado

Facultad de Ciencias

Pontificia Universidad Católica de Valparaíso

Valparaíso, Chile

2
franklin johnson

Director

Computación e Informática

Universidad de Playa Ancha de Ciencias de la Educación

valparaíso, Chile

2
Esteban Gil

Professor

Electrical Engineering

UNIVERSIDAD TECNICA FEDERICO SANTA MARIA

Valparaiso, Chile

2
Pablo Ormeño

DOCENTE

INFORMATICA

UTFSM

VALPARAISO, Chile

2
Cristián Serpell

Estudiante Candidato a Doctorado

Departamento de Informática

Universidad Técnica Federico Santa María

Valparaíso, Chile

1
Marcelo Mendoza

Académico

Ciencias de la Computación

Pontificia Universidad Católica de Chile

Santiago, Chile

1
María Rodríguez

Profesora Asociada

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE

Santiago, Chile

1
Ricardo Soto

Director

Escuela de Ingeniería Informática - PUCV

Valparaiso, Chile

1
Héctor Allende

Profesor Asociado

Pontificia Universidad Católica de Valparaíso

Valparaíso, Chile

1
Rodrigo Salas

Profesor Titular Jornada Completa

UNIVERSIDAD DE VALPARAÍSO

Valparaíso, Chile

1
Antonio Eblen

Profesor carga completa

Facultad de Medicina

Universidad Diego Portales

Santiago, Chile

1
Alejandro Angulo

Académico

Ingeniería Eléctrica

UNIVERSIDAD TECNICA FEDERICO SANTA MARIA

Valparaiso, Chile