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

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

Aus der Reihe Lecture Notes in Computer Science

78,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.09.2024

Abbildungen

XL, 438 p. 177 illus., 165 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

438

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

721 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72358-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

18.09.2024

Abbildungen

XL, 438 p. 177 illus., 165 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

438

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

721 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72358-2

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

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    .- De novo Drug Design – Do We Really Want To Be  “Original”?



    .- Elucidation of Molecular Substructures from Nuclear Magnetic Resonance Spectra using Gradient Boosting.


    .- Neural SHAKE: Geometric Constraints in Graph Generative Models.


    .- Scaffold Splits Overestimate Virtual Screening  Performance.


    .- Target-Aware Drug Activity Model: A deep learning approach to virtual HTS.


    .- Workshop: Reservoir Computing.


    .- Effects of Input Structure and Topology on Input-Driven  Functional Connectivity Stability.


    .- Non-dissipative Reservoir Computing approaches for time-series classification.


    .- Onion Echo State Networks A Preliminary Analysis of Dynamics.


    .- Oscillation-driven Reservoir Computing for Long-Term Replication of Chaotic Time Series.


    .- Prediction of reaching movements with target information towards trans-humeral prosthesis control using Reservoir Computing and LSTMs.


    .- Reducing Reservoir Dimensionality with Phase Space Construction for Simplified Hardware



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    .- Restricted Reservoirs on Heterogeneous Timescales.


    .- Special Session: Accuracy, Stability, and Robustness in Deep Neural Networks.


    .- Clean-image Backdoor Attacks.


    .- MADE: A Universal Fine-tuning Framework to Enhance Robustness of Machine Reading Comprehension.


    .- Robustness of biologically grounded neural networks against image perturbations.


    .- Some Comparisons of Linear and Deep ReLU Network Approximation.


    .- Unlearnable Examples Detection via Iterative Filtering.


    .- Special Session: Neurorobotics.


    .- Action recognition system integrating motion and object detection.


    .- Active Vision for Physical Robots using the Free Energy Principle.


    .- Learning Low-Level Causal Relations using a Simulated Robotic Arm.


    .- Modular Reinforcement Learning In Long-Horizon Manipulation Tasks.


    .- Robotic Model of the Mirror Neuron System: a Revival.


    .- Self-organized attractoring in locomoting animals and robots: an emerging field.


    .- Special Session: Spiking Neural Networks.


    .- A Multi-modal Spiking Meta-learner With Brain-inspired Task-aware Modulation Scheme.


    .- Event-Based Hand Detection on Neuromorphic Hardware Using a Sigma Delta Neural Network.


    .- Learning in Recurrent Spiking Neural Networks with Sparse full-FORCE Training.


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    .- Obtaining Optimal Spiking Neural Network in Sequence Learning via CRNN-SNN Conversion.


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    .- Temporal Contrastive Learning for Spiking Neural



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