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 IV 
T2  - Lecture Notes in Computer Science,
SN  - 9783031723414
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  -  -- Brain-inspired ComputingBrain-inspired Computing.  -- A Multiscale Resonant Spiking Neural Network for Music Classification.  -- Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements.  -- Serial Order Codes for Dimensionality Reduction in the Learning of Higher-Order Rules and Compositionality in Planning.  -- Sparsity aware Learning in Feedback-driven Differential Recurrent Neural Networks.  -- Towards Scalable GPU-Accelerated SNN Training via Temporal Fusion.  -- Cognitive and Computational Neuroscience.  -- Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer.  -- Biologically-plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions.  -- Dynamic Graph for Biological Memory Modeling: A System-Level Validation.  -- EEG features learned by convolutional neural networks reflect alterations of social stimuli processing in autism.  -- Estimate of the Storage Capacity of q-Correlated Patterns in Hopfield Neural Networks.  -- An Accuracy-Shaping Mechanism for Competitive Distributed Learning.  -- Explainable Artificial Intelligence.  -- Counterfactual Contrastive Learning for Fine Grained Image Classification.  -- Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space.  -- Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule Learning.  -- Generally-Occurring Model Change for Robust Counterfactual Explanations.  -- Model Based Clustering of Time Series Utilizing Expert ODEs.  -- Towards Generalizable and Interpretable AI-Modified Image Detectors.  -- Understanding Deep Networks via Multiscale Perturbations.  -- Robotics.  -- Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning.  -- Neural Formation A*: A Knowledge-Data Hybrid-Driven Path Planning Algorithm for Multi-agent Formation Cooperation.  -- Robust Navigation for Unmanned Surface Vehicle Utilizing Improved Distributional Soft Actor-Critic.  -- When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration.  -- Reinforcement Learning.  -- Asymmetric Actor-Critic for Adapting to Changing Environments in Reinforcement Learning.  -- Building surrogate models using trajectories of agents trained by Reinforcement Learning.  -- Demand-Responsive Transport Dynamic Scheduling Optimization Based on Multi-Agent Reinforcement Learning under Mixed Demand.  -- Dual Action Policy for Robust Sim-to-Real Reinforcement Learning.  -- Enhancing Visual Generalization in Reinforcement Learning with Cycling Augmentation.  -- Speeding up Meta-Exploration via Latent Representation
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/10.1007/978-3-031-72341-4
ER  -