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Product details:
- ISBN-10 : 3030168417
- ISBN-13 : 9783030168414
- Author: Luca Oneto
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.
Table of 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?
- 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
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