Jun 14, 2018 · We introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD).

Hierarchical networks consist of a number of loosely-coupled subnets, arranged in layers. Each subnet is intended to capture specific aspects of the input data.

Hierarchical neural-net interpretations (ACD). Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code ...

Jun 18, 2005 · In this paper, we concentrate on the expressive power of hierarchical structures in data analysis. Recently, the so-called Split Net model ...

We propose a neural text classifier with built-in interpretability that can generate hierarchical explanations by identifying both label-dependent and topic- ...

Nov 8, 2017 · A hierarchical network is a network topology that organizes devices in a tree-like structure, with multiple levels of hierarchy. This design ...

In this paper, we concentrate on the expressive power of hierarchical structures in neural net- works. Recently, the so-called SplitNet model.

This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image ...

Aug 31, 2023 · Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the ...

Apr 6, 2023 · A comparison of neural network clustering (NNC) and hierarchical clustering (HC) is conducted to assess computing dominance of two machine learning (ML) ...