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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Mayug Maniparambil
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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posters
Denoising High Density Gene Expression in Whole Mouse Brain Images
Published:
A generative model(GAN) based approach to inpaint serial two photon tomography images of mice brain. In collaboration with Dr. Pavel Osten’s Lab, Cold Spring Harbor Laboratory.
Recommended citation: M. Maniparambil, A. Vadathya, K. Venkataraju, KK. Mitra, P. Osten, “Denoising High Density Gene Expression in Whole Mouse Brain Images” arXiv preprint arXiv:1805.03593, 2017 http://academicpages.github.io/files/deep_fpm_paper.pdf
publications
Phase retrieval for Fourier Ptychography under varying amount of measurements
Published in British Machine Vision Conference, 2018
Deep learning based prior to improve phase retieval in Fourier Ptychography Microscopy. Accepted as spotlight paper at British Machine Vision Conference 2018.
Recommended citation: L. Boominathan, M. Maniparambil, H. Gupta, R. Baburajan, and K. Mitra, “Phase retrieval for fourier ptychography under varying amount of measurements,” arXiv preprint arXiv:1805.03593, 2018 http://academicpages.github.io/files/deep_fpm_paper.pdf
Phase retrieval for Fourier Ptychography under varying amount of measurements
Published in British Machine Vision Conference, 2018
Deep learning based prior to improve phase retieval in Fourier Ptychography Microscopy. Accepted as spotlight paper at British Machine Vision Conference 2018.
Recommended citation: L. Boominathan, M. Maniparambil, H. Gupta, R. Baburajan, and K. Mitra, “Phase retrieval for fourier ptychography under varying amount of measurements,” arXiv preprint arXiv:1805.03593, 2018 http://academicpages.github.io/files/deep_fpm_paper.pdf
BaseTransformers: Attention over base data-points for One Shot Learning
Published in British Machine Vision Conference, 2022
In this paper, we introduce BaseTransformers, a method designed to enhance the feature representations of support instances during meta-test time in few shot classification tasks. By leveraging well-trained feature representations from the base dataset closest to each support instance, our approach significantly improves performance, achieving state-of-the-art results across multiple benchmark datasets and backbone models in the inductive one shot setting. Accepted at British Machine Vision Conference 2022.
Recommended citation: @inproceedings{Maniparambil_2022_BMVC, author = {Mayug Maniparambil and Kevin McGuinness and Noel O Connor}, title = {BaseTransformers: Attention over base data-points for One Shot Learning}, booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022}, publisher = {{BMVA} Press}, year = {2022}, url = {https://bmvc2022.mpi-inf.mpg.de/0482.pdf} } https://bmvc2022.mpi-inf.mpg.de/482/
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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