rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning
    sub title: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II
  name:
    name part: Tetko, Igor V.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Kůrková, Věra.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Karpov, Pavel.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Theis, Fabian.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXX, 807 p. 294 illus., 193 illus. in color. online resource.
  abstract: 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. .
  table of 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 Appro…
  note: edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Optical data processing
  subject:
    topic: Computer organization
  subject:
    topic: Computers
  subject:
    topic: Algorithms
  subject:
    topic: Computer security
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Computer Systems Organization and Communication Networks
  subject:
    topic: Information Systems and Communication Service
  subject:
    topic: Algorithm Analysis and Problem Complexity
  subject:
    topic: Systems and Data Security
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Theoretical Computer Science and General Issues ; 11728
  identifier: 9783030304843
  identifier: https://doi.org/10.1007/978-3-030-30484-3
  record info:
    record creation date: 190904
    record change date: 20191022063353.0
    record identifier: 978-3-030-30484-3