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NODE
Neural Oblivious Decision Ensembles, a hybrid architecture that integrates oblivious decision trees into a neural network to process structured data.
Arbre de Décision Oblivious
A decision tree where all nodes at the same depth use the same feature for partitioning, simplifying the structure and improving differentiability.
Fonction d'Activation Oblivious
A non-linear activation function that emulates the thresholding behavior of a decision tree node, enabling seamless integration in neural networks.
Entraînement End-to-End
An optimization process where the complete NODE architecture, including tree structures and neural weights, is trained simultaneously through backpropagation.
Entropie Binaire Lissée
A regularized version of cross-entropy used in NODE to stabilize training and avoid trivial partitions in decision trees.
Couche de Décision Neuronale
A neural network layer that implements a set of oblivious decision trees, acting as a differentiable decision module.
Partition de Caractéristiques
The process by which NODE selects and divides input features at each depth level of the oblivious tree to maximize information gain.
Régularisation de Complexité
A penalty applied during NODE training to control the number of leaves in trees and prevent overfitting, similar to pruning.
Hybrid Interpretability
The ability of NODE to provide both global (via tree structure) and local (via decision paths) explanations, combining the advantages of both approaches.
Parametric Leaf Function
The output value of a leaf in a NODE tree, which is not a constant but a linear function of the input features, learned during training.
Tree Structure Derivative
The calculation of the gradient with respect to the tree structure itself (feature choices), made possible by the differentiable nature of oblivious trees in NODE.
Neural Tree Ensemble
The final composition of the NODE model, which stacks several neural decision layers to form a powerful ensemble of decision trees.
Soft Routing
Mechanism in NODE where an input sample is distributed to multiple branches of a tree with probabilities, instead of a hard, binary routing.
Gradient Descent Optimization
The primary optimization method for NODE, which simultaneously adjusts the leaf weights and the feature choices of nodes via gradients.
Numerical Stability
A key property of NODE, ensured by activation functions and loss functions specifically designed to avoid gradient problems like saturation or explosion.
Generalization on Tabular Data
The superior performance of NODE on structured datasets, where it effectively captures non-linear interactions between tabular features.
Scalable Architecture
The design of NODE that allows the addition of layers and trees to model increasingly complex relationships without fundamental modification of the algorithm.