… Deep Neural Decision Tree (DNDT) – and explore its connections to each. DNDTs are neural
… a specific decision tree, and is therefore interpretable1. However, as DNDT is realised by …

… , we introduce a stochastic and differentiable decision tree model, which steers the … (deep)
convolutional network. Our model differs from conventional deep networks because a decision …

… a synergistic melting of neural networks and decision trees (DT) we call neural … of deep
neural networks. We now describe briefly the standard univariate and multivariate decision trees, …

… Based on this observation, we propose a novel mapping from decision trees to deep neural
… weights that reflect the decision tree structure. The neural network is subsequently trained …

… The method used was deep neural decision trees (DNDTs), a technique based on decision
trees implemented by deep learning neural networks, which was compared with other …

… Alternatively, we can gain insight into the model’s decision … decisions as in rule-based
models like decision trees. However, existing efforts to fuse deep learning and decision trees …

… a deep neural network makes its decisions, we use the deep neural network to train a decision
tree that mimics the input-output function discovered by the neural network but works in a …

… Deep neural decision forests. In Proceedings of the IEEE international conference on
computer vision, pages 1467–1475, 2015. 1 [10] Mason McGill and Pietro Perona. …

… [35] note that solely optimizing the leaf parameters is a convex optimization problem and
proposed a derivativefree strategy, named deep neural decision forests (DNDFs). DNDFs …

… In the filed of DT-based interpretations, neural decision trees (NDTs) are … trees with latent
representation learned by a trained deep CNN. Their model is termed as deep neural decision …