Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis.

Format:

Book

Contributor:

Tetko, Igor V., editor.

Kůrková, V. (Vera), 1948- editor.

Karpov, Pavel, editor.

Theis, Fabian, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 11728.

Theoretical Computer Science and General Issues ; 11728

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Computer organization.

Computers.

Algorithms.

Computer security.

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Systems Organization and Communication Networks.

Information Systems and Communication Service.

Algorithm Analysis and Problem Complexity.

Systems and Data Security.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Computer Systems Organization and Communication Networks.

Information Systems and Communication Service.

Algorithm Analysis and Problem Complexity.

Systems and Data Security.

Physical Description:

1 online resource (XXX, 807 pages) : 294 illustrations, 193 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. .

Contents:

Adaptive Graph Fusion for Unsupervised Feature Selection

Unsupervised Feature Selection via Local Total-order Preservation

Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines

Joint Dictionary Learning for Unsupervised Feature Selection

Comparison between Filter Criteria for Feature Selection in Regression

CancelOut: A layer for feature selection in deep neural networks

Adaptive-L2 Batch Neural Gas

Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network

Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls

Automatic Augmentation by Hill Climbing

Learning Camera-invariant Representation for Person Re-identification

PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection

Singular Value Decomposition and Neural Networks

PCI: Principal Component Initialization for Deep Autoencoders

Improving Weight Initialization of ReLU and Output Layers

Post-synaptic potential regularization has potential

A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training

Sign Based Derivative Filtering for Stochastic Gradient Descent

Architecture-aware Bayesian Optimization for Neural Network Tuning

Non-Convergence and Limit Cycles in the Adam Optimizer

Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network

Using feature entropy to guide filter pruning for efficient convolutional networks

Simultaneously Learning Architectures and Features of Deep Neural Networks

Learning Sparse Hidden States in Long Short-Term Memory

Multi-objective Pruning for CNNs using Genetic Algorithm

Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence

Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation

Local Normalization Based BN Layer Pruning

On Practical Approach to Uniform Quantization of Non-redundant Neural Networks

Residual learning for FC kernels of convolutional network

A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming

Compute-efficient neural network architecture optimization by a genetic algorithm

Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures

Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization

Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates

A multitask learning neural network for short-term traffic speed prediction and confidence estimation

Central-diffused Instance Generation Method in Class Incremental Learning

Marginal Replay vs Conditional Replay for Continual Learning

Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks

Active Learning for Image Recognition using a Visualization-Based User Interface

Basic Evaluation Scenarios for Incrementally Trained Classifiers

Embedding Complexity of Learned Representations in Neural Networks

Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions

Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification

Fast Approximate Geodesics for Deep Generative Models

Spatial Attention Network for Few-Shot Learning

Routine Modeling with Time Series Metric Learning

Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures

Conditions for Unnecessary Logical Constraints in Kernel Machines

HiSeqGAN: Hierarchical Sequence Synthesis and Prediction

DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting

Transferable Adversarial Cycle Alignment for Domain Adaption

Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets

Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling

Deep Domain Knowledge Distillation for Person Re-identification

A study on catastrophic forgetting in deep LSTM networks

A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification

An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition

Filter Method Ensemble with Neural Networks

Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes

Increasing the Generalisaton Capacity of Conditional VAEs

Playing the Large Margin Preference Game.

Other Format:

Printed edition:

ISBN:

978-3-030-30484-3

9783030304843

Access Restriction:

Restricted for use by site license.