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Artificial Neural Networks and Machine Learning – ICANN 2024 (Record no. 63469)

MARC details
000 -LEADER
fixed length control field 07807nam a22006135i 4500
001 - CONTROL NUMBER
control field 978-3-031-72332-2
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251030113048.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240917s2024 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031723322
-- 978-3-031-72332-2
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-72332-2
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
245 10 - TITLE STATEMENT
Title Artificial Neural Networks and Machine Learning – ICANN 2024
Medium [electronic resource] :
Remainder of title 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I /
Statement of responsibility, etc. edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer Nature Switzerland :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2024.
300 ## - PHYSICAL DESCRIPTION
Extent XXXIII, 480 p. 149 illus., 128 illus. in color.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Lecture Notes in Computer Science,
International Standard Serial Number 1611-3349 ;
Volume/sequential designation 15016
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note -- Theory of Neural Networks and Machine Learning. -- Multi-label Robust Feature Selection via Subspace-Sparsity Learning. -- Nullspace-based metric for classification of dynamical systems and sensors. -- On the Bayesian Interpretation of Robust Regression Neural Networks. -- Probability-Generating Function Kernels for Spherical Data. -- Tailored Finite Point Operator Networks for Interface problems. -- Novel Methods in Machine Learning. -- A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class. -- Adaptive Compression of the Latent Space in Variational Autoencoders. -- Asymmetric Isomap for Dimensionality Reduction and Data Visualization. -- CALICO: Confident Active Learning with Integrated Calibration. -- Improved Multi-hop Reasoning through Sampling and Aggregating. -- Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks. -- Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations. -- Safe Data Resampling Method based on Counterfactuals Analysis. -- Test-Time Augmentation for Traveling Salesperson Problem. -- Novel Neural Architectures. -- Resonator-Gated RNNs. -- Towards a model of associative memory with learned distributed representations. -- Neural Architecture Search. -- Accelerated NAS via pretrained ensembles and multi-fidelity Bayesian Optimization. -- Feature Activation-Driven Zero-Shot NAS: A Contrastive Learning Framework. -- NAS-Bench-Compre: A Comprehensive Neural Architecture Search Benchmark with Customizable Components. -- NAVIGATOR-D3: Neural Architecture search using VarIational Graph Auto-encoder Toward Optimal aRchitecture Design for Diverse Datasets. -- ResBuilder: Automated Learning of Depth with Residual Structures -- Self-Organization. -- A Neuron Coverage-based Self-Organizing Approach for RBFNNs in Multi-Class Classification Tasks. -- Self-Organising Neural Discrete Representation Learning à la Kohonen. -- Neural Processes. -- Combined Global and Local Information Diffusion of Neural Processes. -- Topology of Neural Processes. -- Novel Architectures for Computer Vision. -- DEEPAM: Toward Deeper Attention Module in Residual Convolutional Neural Networks. -- Differentiable Largest Connected Component Layer for Image Mattin. -- Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line Features. -- Transformer Tracker based on Multi-level Residual Perception Structure. .-Multimodal Architectures. -- CAW: Confidence-based Adaptive Weighted Model for Multi-modal Entity Linking. -- Exploring Interpretable Semantic Alignment for Multimodal Machine Translation. -- Fairness in Machine Learning. -- CFP: A Reinforcement Learning Framework for Comprehensive Fairness-Performance Trade-off in Machine Learning.
520 ## - SUMMARY, ETC.
Summary, etc. The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 58441
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65793
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 60157
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 58441
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 66530
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
9 (RLIN) 65689
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wand, Michael.
Relator term editor.
Authority record control number (orcid)0000-0003-0966-7824
-- https://orcid.org/0000-0003-0966-7824
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 84497
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Malinovská, Kristína.
Relator term editor.
Authority record control number (orcid)0000-0001-7638-028X
-- https://orcid.org/0000-0001-7638-028X
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 84498
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Schmidhuber, Jürgen.
Relator term editor.
Authority record control number (orcid)0000-0002-1468-6758
-- https://orcid.org/0000-0002-1468-6758
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 84499
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tetko, Igor V.
Relator term editor.
Authority record control number (orcid)0000-0002-6855-0012
-- https://orcid.org/0000-0002-6855-0012
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 84500
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031723315
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031723339
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Computer Science,
International Standard Serial Number 1611-3349 ;
Volume number/sequential designation 15016
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-72332-2">https://doi.org/10.1007/978-3-031-72332-2</a>
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