In this paper we propose a method to create a Neuron Activation Rate based Bipartite Graph (NARBG) , that can explain the decisions made by the model, based on ...
Based on these neuron activation rates, influential features for the target class prediction are identified. Then a bipartite graph named NARBG is trained using ...
The decisions made by these models are not explainable. In this paper we propose a method to create a Neuron Activation Rate based Bipartite Graph (NARBG) , ...
Dec 5, 2024 · This paper provides a review of recent advances in memristive neural networks (MNNs), focusing on the issues of multi-stability, ...
... In this paper we propose a method to create a Neuron Activation Rate based Bipartite Graph (NARBG) , that can explain the decisions made by the model, based ...
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Sep 3, 2024 · The neuromuscular circuit can be viewed as a bipartite graph formed between motor neurons and muscle fibers. In newborn animals, neurons and ...
Sep 24, 2024 · An approach to improve network interpretability is via clusterability, ie, splitting a model into disjoint clusters that can be studied independently.
This work proposes a method to identify features with predictive information in the input domain by leveraging a bottleneck on the input that only lets ...
Jan 3, 2024 · This survey provides a comprehensive exploration of applications of Topological Data Analysis (TDA) within neural network analysis.
A NN is a collection of layers of interconnected artificial neurons, or nodes, that receive and transmit signals to other neurons. Data is passed into the ...