Artificial Neural Networks and Machine Learning - ICANN 2024
Description
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.
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Content
.- 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.