TY  - BOOK
AU  - Wand,Michael
AU  - Malinovská,Kristína
AU  - Schmidhuber,Jürgen
AU  - Tetko,Igor V.
ED  - SpringerLink (Online service)
TI  - Artificial Neural Networks and Machine Learning - ICANN 2024: 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part X 
T2  - Lecture Notes in Computer Science,
SN  - 9783031723599
AV  - Q334-342 
U1  - 006.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Computers
KW  - Application software
KW  - Computer networks 
KW  - Artificial Intelligence
KW  - Computing Milieux
KW  - Computer and Information Systems Applications
KW  - Computer Communication Networks
N1  -  -- 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-72359-9
ER  -