Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

transdermal

Counterfactual Contrastive Learning for Fine Grained Image Classificationse approaches typically fall short in addressing the deeper causal relationships that underlie the visible features, leading to potential biases and limited generalizability. This paper presents a fine-grained causal contrastive network (FCCN), a novel architecture that integrates causal inference w

HARP

独轮车

Overdose

Generally-Occurring Model Change for Robust Counterfactual Explanationsng. Counterfactual explanation is an important method in the field of interpretable machine learning, which can not only help users understand why machine learning models make specific decisions, but also help users understand how to change these decisions. Naturally, it is an important task to stud

有害处

Model Based Clustering of Time Series Utilizing Expert ODEsrs in the parameter space (. healthy vs. diseased patients). The problem of identifying these clusters and that of identifying the model parameters are tightly coupled. In this work, we propose a novel model-based clustering method that makes it possible to utilize expert knowledge in the form of pa

Audiometry

Conference proceedings 2024ne 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 machin

Pessary

Nostalgia

音乐等

A Multiscale Resonant Spiking Neural Network for Music Classificatione proposed the Multiscale Resonance SNN model that can comprehensively utilize the rich musical temporal information. With only binary activated neurons and sparse information flows, our model have achieved comparable music classification performance in various datasets.

RUPT

Serial Order Codes for Dimensionality Reduction in the Learning of Higher-Order Rules and Compositiopolate sequences of items from the given repertoire. We demonstrate how this framework can be used to make the solver robust to exponentially growing complexity of the given task by reducing its dimensionality.