12 citations · 13 across the 2 of their papers we have counts for
7 papers
Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering
Behzad Ghazanfari, Fatemeh Afghah
This paper introduces Multi-Level feature learning alongside the Embedding layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering. We use agglomerative…
Piece-wise Matching Layer in Representation Learning for ECG Classification
Behzad Ghazanfari, Fatemeh Afghah, Sixian Zhang
This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of…
Deep Inverse Feature Learning: A Representation Learning of Error
Behzad Ghazanfari, Fatemeh Afghah
This paper introduces a novel perspective about error in machine learning and proposes inverse feature learning (IFL) as a representation learning approach that learns a set of hig…
Inverse Feature Learning: Feature learning based on Representation Learning of Error
Behzad Ghazanfari, Fatemeh Afghah, MohammadTaghi Hajiaghayi
This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error represen…
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian +3
The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients'…
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach
Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. Decomposition of tasks into a hierarchi…