activity
20142024
most citedHierarchical Mixtures of Generators for Adversarial Learning

4 citations · 6 across the 5 of their papers we have counts for

collaborators

8 papers

stat.ML2024★ 2 cited

Introduction to Machine Learning

Laurent Younes

This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning. It starts wi…

cs.LG2022

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…

cs.LG2019★ 4 cited

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…

cs.LG2018

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…

cs.LG2018

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…

cs.LG2014

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…

cs.LG2014

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…

stat.ML2014

Multivariate Comparison of Classification Algorithms

Olcay Taner Yildiz, Ethem Alpaydin

Statistical tests that compare classification algorithms are univariate and use a single performance measure, e.g., misclassification error, measure, AUC, and so on. In multiva…