To see the full list of publications, please refer to my Google Scholar page.
COR-GAN: Correlation-Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records
In COR-GAN we utilize Convolutional Neural Networks to capture the correlations between adjacent medical features in the data representation space by combining Convolutional Generative Adversarial Networks and Convolutional Autoencoders. To demonstrate the model fidelity, we show that COR-GAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
Amirsina Torfi, Edward A. Fox
ArXiv preprint/Accepted to be published in the 33rd International FLAIRS Conference, AI in Healthcare Informatics
Nearest Neighbor Classifier – From Theory to Practice
The K-nearest neighbors (KNNs) classifier or simply Nearest Neighbor Classifier is a kind of supervised machine learning algorithm that operates based on spatial distance measurements. In this article, we investigate the theory behind it. Furthermore, a working example of the k-nearest neighbor classifier will be represented.
Machine Learning Mindset
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
In this paper, we propose Guided Attention
for Sparsity Learning (GASL) to achieve (1) model compression by having less number of elements and speedup; (2) prevent the accuracy drop by supervising the sparsity operation via a guided attention mechanism and (3) introduce a generic mechanism that can be adapted for any type of architecture
Amirsina Torfi, Rouzbeh A. Shirvani, Sobhan Soleymani, Naser M. Nasrabadi
Preprint on ArXiv
Generating Synthetic Healthcare Records Using Convolutional Generative Adversarial Networks
The present study makes several unique contributions to synthetic data generation in the healthcare domain. First, utilizing 1-D Convolutional Neural Networks (CNNs), we devise a new approach to capturing the correlation between adjacent diagnosis records. Second, we employ convolutional autoencoders to map the discrete-continuous values…
Amirsina Torfi, Mohammadreza Beki (equal contribution)
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speaker utterances and creation of the speaker model. This leads to simultaneously capturing the speaker-related information and building a more robust system to cope with a within-speaker variation.
Amirsina Torfi, Nasser M. Nasrabadi, Jeremy Dawson
2018 IEEE International Conference on Multimedia and Expo (ICME)
SpeechPy – A Library for Speech Processing and Recognition
SpeechPy is an open-source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides the most frequently used speech features including MFCCs and filterbank energies alongside the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification.
Journal of Open Source Software
3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
We propose the use of a coupled 3D Convolutional Neural Network (3D-CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities.
Amirsina Torfi, Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi, Jeremy Dawson