4 citations · 6 across the 5 of their papers we have counts for
6 papers · 1 filter
PathFinder: Discovering Decision Pathways in Deep Neural Networks
Ozan İrsoy, Ethem Alpaydın
Explainability is becoming an increasingly important topic for deep neural networks. Though the operation in convolutional layers is easier to understand, processing becomes opaque…
Hierarchical Mixtures of Generators for Adversarial Learning
Alper Ahmetoğlu, Ethem Alpaydın
Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. T…
Dropout Regularization in Hierarchical Mixture of Experts
Ozan İrsoy, Ethem Alpaydın
Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts i…
Continuously Constructive Deep Neural Networks
Ozan İrsoy, Ethem Alpaydın
Traditionally, deep learning algorithms update the network weights whereas the network architecture is chosen manually, using a process of trial and error. In this work, we propose…
Distributed Decision Trees
Ozan İrsoy, Ethem Alpaydın
Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous pa…
Autoencoder Trees
Ozan İrsoy, Ethem Alpaydın
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft mult…