Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 Luca 3030168417 9783030168414

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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:

1. On the Trade-Off Between Number of Examples and Precision of Supervision in Regression
2. Distributed SmSVM Ensemble Learning
3. Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach
4. Fast Transfer Learning for Image Polarity Detection
5. Dropout for Recurrent Neural Networks
6. Psychiatric Disorders Classification with 3D Convolutional Neural Networks
7. Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
8. Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
9. Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks
10. An Information Theoretic Approach to the Autoencoder
11. Deep Regression Counting: Customized Datasets and Inter-Architecture Transfer Learning
12. Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies
13. Presumable Applications of Deep Learning for Cellular Automata Identification
14. Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability
15. Train Overtaking Prediction in Railway Networks: A Big Data Perspective
16. Cavitation Noise Spectra Prediction with Hybrid Models
17. Pseudoinverse Learners: New Trend and Applications to Big Data
18. Innovation Capability of Firms: A Big Data Approach with Patents
19. Predicting Future Market Trends: Which Is the Optimal Window?
20. Modeling Using DNN for Arabic Parametric Speech Synthesis
21. Regularizing Neural Networks with Gradient Monitoring
22. Visual Analytics for Supporting Conflict Resolution in Large Railway Networks
23. Modeling Urban Traffic Data Through Graph-Based Neural Networks
24. Traffic Sign Detection Using R-CNN
25. Deep Tree Transductions – A Short Survey
26. Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks
27. A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification
28. Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors
29. Using Convolutional Neural Networks to Distinguish Different Sign Language Alphanumerics
30. Mise en abyme with Artificial Intelligence: How to Predict the Accuracy of NN, Applied to Hyper-parameter Tuning
31. Asynchronous Stochastic Variational Inference
32. Probabilistic Bounds for Binary Classification of Large Data Sets
33. Multikernel Activation Functions: Formulation and a Case Study
34. Understanding Ancient Coin Images
35. Effects of Skip-Connection in ResNet and Batch-Normalization on Fisher Information Matrix
36. Skipping Two Layers in ResNet Makes the Generalization Gap Smaller than Skipping One or No Layer
37. A Preference-Learning Framework for Modeling Relational Data
38. Convolutional Neural Networks for Twitter Text Toxicity Analysis
39. Fast Spectral Radius Initialization for Recurrent Neural Networks

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