Abhinav Sagar

Email: abhinavsagar4@gmail.com
Address: 64 Greens Radius Developers, Santacruz, Mumbai, India

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Hi! I completed my undergrad in Mechanical Engineering at Vellore Institute of Technology. My research areas are generative models, medical imaging and autonomous driving. More broadly, I am interested in deep learning and computer vision. The communities I follow are NeurIPS, CVPR, ICCV, ECCV, BMVC, WACV and MICCAI.

If you are interested in doing a research collaboration, please contact me.

My hobbies are travelling, playing guitar and cooking (also eating).

LinkedIn  /  Github  /  Google Scholar  /  Medium

Areas of Interest

1. Efficient neural network architectures

2. Uncertainty quantification

3. Generative models

4. Semantic/Instance segmentation

5. Attention mechanism

6. Drug discovery

News

[6th June 2021] 1 paper accepted at Distribution-Free Uncertainty Quantification workshop (ICML 2021).

[31st May 2021] 1 paper accepted at Artificial Intelligence for Autonomous Driving workshop (IJCAI 2021).

[23th May 2020] Successfully defended my bachelors thesis.

[5th Dec 2019] Will be assisting Professor Madhu Viswanatham as teaching assistant for course CSE1002 (Object Oriented Programming).

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

[2nd Dec 2018] Will be assisting Professor Gayathri P as teaching assistant for course CSE2003 (Data Structures and Algorithm).

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

Research Papers and Preprints

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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Bayesian Multi Scale Neural Network for Crowd Counting

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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DMSANet: Dual Multi Scale Attention Network

Paper |  Code |  BibTeX
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