Hi fellow Kagglers,
\nI have found some previous work done in the astrophysics domain on working with radio signals using Deep Learning Methods. Please find the links below:-
I have also published a starter kernel using Pytorch and NFNet. It has taken some inspiration from the 1st paper here on the list.
\nI will add further relevant resources here if and when I find them. Hope you find these useful.
\nThanks. 😊
", "messages": [ { "id": 1308748, "postDate": "2021-05-15T12:40:30.640Z", "content": "Hi fellow Kagglers,
\nI have found some previous work done in the astrophysics domain on working with radio signals using Deep Learning Methods. Please find the links below:-
I have also published a starter kernel using Pytorch and NFNet. It has taken some inspiration from the 1st paper here on the list.
\nI will add further relevant resources here if and when I find them. Hope you find these useful.
\nThanks. 😊
", "rawMarkdown": "Hi fellow Kagglers,\nI have found some previous work done in the astrophysics domain on working with radio signals using Deep Learning Methods. Please find the links below:-\n* [Machine Vision and Deep Learning for Classification of Radio SETI Signals](https://arxiv.org/ftp/arxiv/papers/1902/1902.02426.pdf)\n* [Classify radio signals from space](https://www.ijariit.com/manuscripts/v6i3/V6I3-1559.pdf)\n* [Search for extra terrestrial intelligence (SETI) with Tensorflow on PowerAI](https://github.com/IBM/powerai-seti-signal-classification)\n* [Classifying Radio Galaxies with the Convolutional Neural Network](https://iopscience.iop.org/article/10.3847/1538-4365/aa7333)\n* [THE BREAKTHROUGH LISTEN SEARCH FOR INTELLIGENT LIFE: PUBLIC DATA, FORMATS,\nREDUCTION AND ARCHIVING](https://arxiv.org/pdf/1906.07391.pdf)\n\nI have also published a [starter kernel using Pytorch and NFNet](https://www.kaggle.com/manabendrarout/nfnet-pytorch-starter-lb-0-95). It has taken some inspiration from the 1st paper here on the list.\n\nI will add further relevant resources here if and when I find them. Hope you find these useful.\n\nThanks. 😊", "votes": 38 } ], "comments": [], "raw_markdown_by_id": { "1308748": "Hi fellow Kagglers,\nI have found some previous work done in the astrophysics domain on working with radio signals using Deep Learning Methods. Please find the links below:-\n* [Machine Vision and Deep Learning for Classification of Radio SETI Signals](https://arxiv.org/ftp/arxiv/papers/1902/1902.02426.pdf)\n* [Classify radio signals from space](https://www.ijariit.com/manuscripts/v6i3/V6I3-1559.pdf)\n* [Search for extra terrestrial intelligence (SETI) with Tensorflow on PowerAI](https://github.com/IBM/powerai-seti-signal-classification)\n* [Classifying Radio Galaxies with the Convolutional Neural Network](https://iopscience.iop.org/article/10.3847/1538-4365/aa7333)\n* [THE BREAKTHROUGH LISTEN SEARCH FOR INTELLIGENT LIFE: PUBLIC DATA, FORMATS,\nREDUCTION AND ARCHIVING](https://arxiv.org/pdf/1906.07391.pdf)\n\nI have also published a [starter kernel using Pytorch and NFNet](https://www.kaggle.com/manabendrarout/nfnet-pytorch-starter-lb-0-95). It has taken some inspiration from the 1st paper here on the list.\n\nI will add further relevant resources here if and when I find them. Hope you find these useful.\n\nThanks. 😊" } }