5 citations · 6 across the 6 of their papers we have counts for
6 papers
Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder
Surojit Saha, Albert K. H Kong
The coalescence of binary neutron stars in the GW170817 event led to the generation of gravitational waves, accompanied by the electromagnetic counterpart known as a kilonova (KN).…
Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions
Surojit Saha, Sarang Joshi, Ross Whitaker
Deep latent variable models (DLVMs) are designed to learn meaningful representations in an unsupervised manner, such that the hidden explanatory factors are interpretable by indepe…
ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders
Surojit Saha, Sarang Joshi, Ross Whitaker
The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimiz…
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
Surojit Saha, Ross Whitaker
Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-shot learning is a suitable learning…
Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies
Xiwen Li, Rehman Mohammed, Tristalee Mangin +4
Idling vehicle detection (IVD) can be helpful in monitoring and reducing unnecessary idling and can be integrated into real-time systems to address the resulting pollution and harm…
Matching aggregate posteriors in the variational autoencoder
Surojit Saha, Sarang Joshi, Ross Whitaker
The variational autoencoder (VAE) is a well-studied, deep, latent-variable model (DLVM) that efficiently optimizes the variational lower bound of the log marginal data likelihood a…