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Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part X /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Neural Networks and Machine Learning - ICANN 2024/ edited by Michael Wand ... [et al.].
其他題名:	33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.
其他題名:	ICANN 2024
其他作者:	Wand, Michael.
團體作者:	Workshop on the Preservation of Stability under Discretization
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xl, 438 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Computer Communication Networks. -
電子資源:	https://doi.org/10.1007/978-3-031-72359-9
ISBN:	9783031723599

Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part X /
Artificial Neural Networks and Machine Learning - ICANN 202433rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.Part X /[electronic resource] :ICANN 2024edited by Michael Wand ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xl, 438 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,150250302-9743 ;. - Lecture notes in computer science,7131. .

Workshop: AI in Drug Discovery. -- Combinatorial Library Neural Network (CoLiNN) for Combinatorial Library Visualization without Compound Enumeration. -- 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 Implementation. -- 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. -- Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices. -- Obtaining Optimal Spiking Neural Network in Sequence Learning via CRNN-SNN Conversion. -- On Reducing Activity with Distillation and Regularization for Energy Ecient Spiking Neural Networks. -- Temporal Contrastive Learning for Spiking Neural Networks.

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.

ISBN: 9783031723599

Standard No.: 10.1007/978-3-031-72359-9doiSubjects--Topical Terms:

669310
Computer Communication Networks.

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Artificial Neural Networks and Machine Learning - ICANN 2024 = 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024 : proceedings.. Part X /
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