Recent Advances in Big Data and Deep Learning
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Основная информация:
Название: Recent Advances in Big Data and Deep Learning
Жанр: Нет
Автор: Luca Oneto, Nicolo Navarin
Год выпуска: 2019 (2020 Edition)
Формат: PDF
Размер: 22.1 MB
ISBN: 469449637690
Язык: Английский
СКАЧАТЬ Recent Advances in Big Data and Deep Learning БЕСПЛАТНО EPUB - DOC - DJVU - RTF - PDFОписание: This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18.More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019.
In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.
Contents:
On the Trade-Off Between Number of Examples and Precision of Supervision in Regression
Distributed SmSVM Ensemble Learning
Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach
Fast Transfer Learning for Image Polarity Detection
Dropout for Recurrent Neural Networks
Psychiatric Disorders Classification with 3D Convolutional Neural Networks
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks
An Information Theoretic Approach to the Autoencoder
Deep Regression Counting: Customized Datasets and Inter-Architecture Transfer Learning
Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies
Presumable Applications of Deep Learning for Cellular Automata Identification
Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability
Train Overtaking Prediction in Railway Networks: A Big Data Perspective
Cavitation Noise Spectra Prediction with Hybrid Models
Pseudoinverse Learners: New Trend and Applications to Big Data
Innovation Capability of Firms: A Big Data Approach with Patents
Predicting Future Market Trends: Which Is the Optimal Window?
F 0 Modeling Using DNN for Arabic Parametric Speech Synthesis
Regularizing Neural Networks with Gradient Monitoring
Visual Analytics for Supporting Conflict Resolution in Large Railway Networks
Modeling Urban Traffic Data Through Graph-Based Neural Networks
Traffic Sign Detection Using R-CNN
Deep Tree Transductions - A Short Survey
Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks
A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification
Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors
Using Convolutional Neural Networks to Distinguish Different Sign Language Alphanumerics
Mise en abyme with Artificial Intelligence: How to Predict the Accuracy of NN, Applied to Hyper-parameter Tuning
Asynchronous Stochastic Variational Inference
Probabilistic Bounds for Binary Classification of Large Data Sets
Multikernel Activation Functions: Formulation and a Case Study
Understanding Ancient Coin Images
Effects of Skip-Connection in ResNet and Batch-Normalization on Fisher Information Matrix
Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer
A Preference-Learning Framework for Modeling Relational Data
Convolutional Neural Networks for Twitter Text Toxicity Analysis
Fast Spectral Radius Initialization for Recurrent Neural Networks
Author Index . . . . . 391