Topic: explainability

IJCNN covers neural-network foundations and intelligent applications, including continual and adaptive learning, forgetting mitigation, representation learning, explainability, neuromorphic computation, computational neuroscience and multimodal models. Research papers connect theoretical methods with applications across science and engineering. Review is double-blind. Standard submissions use six IEEE-format pages; up to four extra pages incur fees. Accepted authors must submit final papers and pay the corresponding registration fees by 12 April 2027, 23:59 AoE. Remote authors may supply prer

In this talk, Jonathan aims to frame the current challenges of explainability and understanding in ML-driven approaches to image processing, and their potential solution through explicit inference techniques.