65,40 €*
Versandkostenfrei per Post / DHL
Lieferzeit 2-4 Werktage
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.
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.
.- 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.
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Titelzusatz: | 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17¿20, 2024, Proceedings, Part IV |
Reihe: | Lecture Notes in Computer Science |
Inhalt: |
xxxiv
428 S. 2 s/w Illustr. 136 farbige Illustr. 428 p. 138 illus. 136 illus. in color. |
ISBN-13: | 9783031723407 |
ISBN-10: | 3031723406 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Redaktion: |
Wand, Michael
Tetko, Igor V. Schmidhuber, Jürgen Malinovská, Kristína |
Herausgeber: | Michael Wand/Kristína Malinovská/Jürgen Schmidhuber et al |
Hersteller: |
Springer Nature Switzerland
Lecture Notes in Computer Science |
Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 25 mm |
Von/Mit: | Michael Wand (u. a.) |
Erscheinungsdatum: | 01.10.2024 |
Gewicht: | 0,698 kg |
.- 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.
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Titelzusatz: | 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17¿20, 2024, Proceedings, Part IV |
Reihe: | Lecture Notes in Computer Science |
Inhalt: |
xxxiv
428 S. 2 s/w Illustr. 136 farbige Illustr. 428 p. 138 illus. 136 illus. in color. |
ISBN-13: | 9783031723407 |
ISBN-10: | 3031723406 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Redaktion: |
Wand, Michael
Tetko, Igor V. Schmidhuber, Jürgen Malinovská, Kristína |
Herausgeber: | Michael Wand/Kristína Malinovská/Jürgen Schmidhuber et al |
Hersteller: |
Springer Nature Switzerland
Lecture Notes in Computer Science |
Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
Maße: | 235 x 155 x 25 mm |
Von/Mit: | Michael Wand (u. a.) |
Erscheinungsdatum: | 01.10.2024 |
Gewicht: | 0,698 kg |