no code implementations • 20 Sep 2020 • Rahul Nigam, Amit Mishra, Pranath Reddy
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.
no code implementations • 4 Nov 2019 • Leendert A Remmelzwaal, Amit Mishra, George F R Ellis
A key point in this paper is that the CNN does not need to be trained to identify rotation or scaling permutations; rather it is the log-polar pre-processing step that converts the image into a format that allows the CNN to handle rotation and scaling permutations.
no code implementations • 11 Aug 2019 • Amit Mishra, Pranath Reddy, Rahul Nigam
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.
no code implementations • 28 Mar 2019 • Amit Mishra, Pranath Reddy, Rahul Nigam
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
no code implementations • 8 Nov 2018 • Jarryd Son, Amit Mishra
Perceptual capabilities of artificial systems have come a long way since the advent of deep learning.
no code implementations • 23 Nov 2017 • Daniel Czech, Amit Mishra, Michael Inggs
Transient (impulsive) RFI is particularly difficult to identify.