Keynote speakers

Comparison of Graph Neural Network Architectures for Predicting the Electronic Structure of Molecules and Solids

Concept-Based Explainable Deep Learning Models

[introductory/intermediate] Natural Language Processing in the Large Language Model Era

[intermediate/advanced] Deep Learning in Science

[introductory/intermediate] Multi-Tasking Machine Learning in Drug and Materials Design

[intermediate] Deep Networks for 3D Computer Vision

[introductory/intermediate] Quantum Machine Learning on Parameterized Quantum Circuits

[intermediate/advanced] Learning from Unreliable Labels via Crowdsourcing

[intermediate/advanced] Deep 3D Pose Estimation

[intermediate/advanced] Methods for Learning with Few Data

[introductory/intermediate] Image and Video Restoration

[introductory/intermediate] Tensor Methods for Approximation of High-Dimensional Arrays and Their Applications in Machine Learning

[intermediate/advanced] Continually Improving Agents for Generalization in the Wild

[introductory/advanced] Neuromorphic Computing

[introductory/intermediate] Deep Multimedia Processing

[intermediate] Meta-Learning and Contrastive Learning for Robust Representations

[introductory/intermediate] Randomization-Based Deep and Shallow Learning Algorithms and Architectures

[introductory/advanced] Deep Learning on Graphs: Methods, Advances and Applications

[intermediate] Applications of Graph Neural Networks: Physical and Societal Systems

[introductory] Deep Learning and Privacy Enhancing Technology

[introductory] Introduction to Computer Vision and the Ethical Questions It Raises