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Application of Unsupervised Domain Adaptation for Structural MRI Analysis
The primary goal of this work is to study the effectiveness of an unsupervised domain adaptation approach for various applications such as binary classification and anomaly detection in the context of Alzheimer's disease (AD) detection for the OASIS datasets.
Transient Classification in low SNR Gravitational Wave data using Deep Learning
We also use unsupervised models such as Total variation, Principal Component Analysis, Support Vector Machine, Wavelet decomposition or Random Forests for feature extraction and noise reduction and then study the results obtained by RNN-LSTM and deep CNN for classifying the transients in low-SNR signals.
CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning
We train our deep generative model to learn the complex distribution of CMB maps and efficiently generate new sets of CMB data in the form of 2D patches of anisotropy maps without losing much accuracy.
Baryon density extraction and isotropy analysis of Cosmic Microwave Background using a multilayer perceptron
We correlate the baryon density obtained from the power spectrum of CMB temperature maps with the corresponding map and form the dataset for training the neural network model.
Cosmology and Nongalactic Astrophysics
