ICANN 2024 is a conference featuring tracks in Artificial Intelligence and Machine Learning, Bio-inspired computing, and applications, with strong cross-disciplinary interactions. All research fields dealing with Neural Networks will be present at the conference. A non-exhaustive list of topics includes the following.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Theory and Foundations of Neural Networks
NN Convergence and Generalization, Loss Landscape Analysis
Novel Neural Network Architectures
Innovation in Recurrent and Transformer Architectures, Dynamical Models, Physically Informed NNs, Novel Learning Algorithms
Topics in Neural Networks
Generative Networks, Adaptation and Transfer Learning, Zero-Shot Learning, Lifelong learning, Knowledge Distillation, Representation Learning, Self-Supervised Learning, Contrastive Learning
Explainable AI
Local and Global Explanations, Feature Attribution, Saliency Methods, Circuits, Counterfactuals
Graph Methods, Graph Neural Networks, and Clustering
Bayesian Networks, Graph neural networks
Reinforcement Learning
Deep Reinforcement Learning, Policy Gradients, Actor-Critic Methods, Artificial Curiosity
Topics in Artificial Intelligence and Machine Learning
Statistical Methods, Kernel Methods, Information Theoretic Learning, Ethics of AI, Conscious AI

BIO-INSPIRED COMPUTING

Brain-Inspired Computing
Cognitive Models, Self-organization, Spiking Neural Networks, Hybrid Neural-Symbolic Architectures, Reservoir Computing, Neuromorphic Computing
Cognitive and Theoretical/Computational Neuroscience
Brain Informatics, Neural Dynamics, Neural Control and Planning, Cognitive Models, Biophysical models, Learning algorithms

APPLICATIONS

Human-Computer Interaction
Biosignal Processing, Medicine and Physiology
Speech and Language Processing
Computer Vision
Robotics
General Applications of Machine Learning
Climate and Environment

SPECIAL SESSION: AI IN DRUG DISCOVERY

Big Data and advanced machine learning in chemistry
eXplainable AI (XAI) in chemistry
Chemoinformatics
Use of deep learning to predict molecular properties
Modeling and prediction of chemical reaction data
Generative models