Abhinav Sagar

Email: abhinavsagar4 at gmail dot com
Address: 64 Greens Radius Developers, Santacruz, Mumbai, India

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Hi! I just completed my undergrad at VIT Vellore. My research areas are bayesian deep learning, generative models, variational inference etc on the theoretical side and medical imaging, autonomous driving etc on the application side. More broadly, I am interested in deep learning and computer vision with a bayesian approach taking uncertainty into account. The communities I follow are NeurIPS, ICLR, CVPR, ICCV, ECCV, MICCAI, MIDL, WACV and UAI.

I spent the summer of 2019 in Tessact where I worked on product recommendation using variational autoencoders. I spent the summer of 2018 in Tata Group where I trained a neural network on power grid electricity consumption data to predict the load 24 hours ahead of the actual generation.

Besides research, I enjoy travelling, playing guitar and cooking (also eating).

CV  /  Linkedin  /  Medium  /  Github  /  Twitter  /  Google Scholar

If you would like to do a research collaboration, please send me an email.

News

[6th Jan 2020] Appointed as teaching assistant for CSE4020 (Machine Learning) with Professor Gayathri P.

[14th Sep 2019] Speaking on Automated Machine Learning at RMZ Millenia Business Park in Chennai, India.

[3rd Jun 2019] Attending the Nordic Probabilistic AI School in Trondheim, Norway with full travel grant.

[8th Apr 2019] Got selected for Computer Vision internship at Tessact in Mumbai, India.

[6th Oct 2018] Speaking on Ethics of Artificial Intelligence at Channa Reddy Auditorium in Vellore, India.

[15th May 2018] Got selected for Deep Learning internship at Tata Group in Jamshedpur, India.

Research Papers and Preprints

Generate High Resolution Images With Generative Variational Autoencoder

Paper |  Code |  BibTeX
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Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

Paper |  Code |  BibTeX
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Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars

Paper |  Code |  BibTeX
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Stochastic Bayesian Neural Networks

Paper |  Code |  BibTeX
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Bayesian Multi Scale Neural Network for Crowd Counting

Paper |  Code |  BibTeX
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RUHSNet: 3D Object Detection Using Lidar Data in Real Time

Paper |  Code |  BibTeX
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HRVGAN: High Resolution Video Generation using Spatio-Temporal GAN

Paper |  Code |  BibTeX
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Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion

Paper |  Code |  BibTeX
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Generate Novel Molecules With Target Properties Using Conditional Generative Models

Paper |  Code |  BibTeX
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