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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15016

Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part I

Aus der Reihe Lecture Notes in Computer Science

78,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 480 p. 149 illus., 128 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

480

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

774 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72331-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 480 p. 149 illus., 128 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

480

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

774 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72331-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024

  • .- 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.