Výsledky vyhledávání - "Ghazanfari, Behzad"

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Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering

Autor: Ghazanfari, Behzad, Afghah, Fatemeh

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 clustering as the multi-level feature learning that provides a hierar

Externí odkaz: http://arxiv.org/abs/2010.02343

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Piece-wise Matching Layer in Representation Learning for ECG Classification

Autor: Ghazanfari, Behzad, Afghah, Fatemeh, Zhang, Sixian

This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of representation learning methods in the analysis of time series, there

Externí odkaz: http://arxiv.org/abs/2010.06510

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Deep Inverse Feature Learning: A Representation Learning of Error

Autor: Ghazanfari, Behzad, Afghah, Fatemeh

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 high-level features based on the representation of error for classificati

Externí odkaz: http://arxiv.org/abs/2003.04285

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Inverse Feature Learning: Feature learning based on Representation Learning of Error

Autor: Ghazanfari, Behzad, Afghah, Fatemeh, Hajiaghayi, MohammadTaghi

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 representation approach. The key contribution of this method is to learn the r

Externí odkaz: http://arxiv.org/abs/2003.03689

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An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs

Autor: Ghazanfari, Behzad, Afghah, Fatemeh, Najarian, Kayvan, Mousavi, Sajad, Gryak, Jonathan, Todd, James

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' movements, detachment of monitoring sensors, or different sources of

Externí odkaz: http://arxiv.org/abs/1904.08495

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Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach

Autor: Ghazanfari, Behzad, Afghah, Fatemeh, Taylor, Matthew E.

Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. Decomposition of tasks into a hierarchical structure holds the potential to significantly speed up learning,

Externí odkaz: http://arxiv.org/abs/1811.08275

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Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning

Autor: Ghazanfari, Behzad, Taylor, Matthew E.

Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learn

Externí odkaz: http://arxiv.org/abs/1709.04579

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