As you might already know, this competition was hosted in 2021 NeurIPS as well. Winners presented their solutions in the workshop remotely. You can find the recording here:
\nhttps://drive.google.com/file/d/1aQss-KyfYlzdrBQcH5joiXMlTwpG5gdf/view?usp=sharing
\nSource code of winning solutions can be found here:
\nhttps://github.com/openproblems-bio/neurips2021_multimodal_topmethods
As you might already know, this competition was hosted in 2021 NeurIPS as well. Winners presented their solutions in the workshop remotely. You can find the recording here:
\nhttps://drive.google.com/file/d/1aQss-KyfYlzdrBQcH5joiXMlTwpG5gdf/view?usp=sharing
\nSource code of winning solutions can be found here:
\nhttps://github.com/openproblems-bio/neurips2021_multimodal_topmethods
These links for the last year competition could also be helpful:
\nhttps://openproblems.bio/neurips_docs/data/about_multimodal/
\nhttps://openproblems.bio/neurips_docs/about_tasks/task1_modality_prediction/
\nhttps://youtu.be/Fm-MDpPo85c
\nhttps://youtu.be/ZXDILOyiy7A
Thanks @jiweiliu 😊!
", "rawMarkdown": "Thanks @jiweiliu 😊!", "votes": null }, { "id": "1920057", "postDate": "08/30/2022 20:41:53", "content": "Thank you!
", "rawMarkdown": "Thank you!", "votes": null }, { "id": "1920953", "postDate": "08/31/2022 13:44:00", "content": "This is great.
", "rawMarkdown": "This is great.", "votes": null }, { "id": "1921290", "postDate": "08/31/2022 17:29:43", "content": "The information in these links have been insightful. Thank you.
", "rawMarkdown": "The information in these links have been insightful. Thank you.", "votes": null }, { "id": "1921915", "postDate": "09/01/2022 06:32:23", "content": "@jiweiliu Just as a sanity check, here Multiome and CITEseq corresponds to the ATAC->GEX and GEX->ADT subtasks respectively in the 2021 NeurIPS competition?
", "rawMarkdown": "jiweiliu Just as a sanity check, here Multiome and CITEseq corresponds to the ATAC->GEX and GEX->ADT subtasks respectively in the 2021 NeurIPS competition?", "votes": null }, { "id": "1922656", "postDate": "09/01/2022 16:07:51", "content": "yes, exactly.
", "rawMarkdown": "yes, exactly.", "votes": null }, { "id": "1923013", "postDate": "09/01/2022 23:08:56", "content": "Thanks, and congrats on Amex, autogressive features are such a cool idea.
", "rawMarkdown": "Thanks, and congrats on Amex, autogressive features are such a cool idea.", "votes": null }, { "id": "1923097", "postDate": "09/02/2022 02:17:38", "content": "Very useful info. Thanks!!!
", "rawMarkdown": "Very useful info. Thanks!!!", "votes": null }, { "id": "1923098", "postDate": "09/02/2022 02:19:39", "content": "Thank you :)
", "rawMarkdown": "Thank you :)", "votes": null }, { "id": "1923260", "postDate": "09/02/2022 05:15:05", "content": "Thanks for sharing this @jiweiliu
", "rawMarkdown": "Thanks for sharing this @jiweiliu", "votes": null }, { "id": "1924609", "postDate": "09/03/2022 08:18:41", "content": "Thank you so much. It is very useful.
", "rawMarkdown": "Thank you so much. It is very useful.", "votes": null }, { "id": "1925882", "postDate": "09/04/2022 12:14:36", "content": "Thanks, these are great resources!
", "rawMarkdown": "Thanks, these are great resources!", "votes": null }, { "id": "1926285", "postDate": "09/04/2022 17:48:43", "content": "https://youtu.be/i6s38LGKtX4
\nKsenia Petrova gives overview of some solutions of the previous year competition.
\nPS
\nFor the other talks:
\nsee https://www.kaggle.com/competitions/open-problems-multimodal/discussion/348661
Thanks for your sharing!
", "rawMarkdown": "Thanks for your sharing!", "votes": null }, { "id": "1930779", "postDate": "09/08/2022 07:25:07", "content": "To any newbies like me, I suggest starting with the competition summary paper. I believe this competition is the same as Task 1 of the 2021 competition, so you could just focus on that part.
", "rawMarkdown": "To any newbies like me, I suggest starting with the competition summary [paper](https://proceedings.mlr.press/v176/lance22a.html). I believe this competition is the same as Task 1 of the 2021 competition, so you could just focus on that part.", "votes": null }, { "id": "2028538", "postDate": "11/14/2022 03:25:29", "content": "thanks for this!
", "rawMarkdown": "thanks for this!", "votes": null } ], "comments": [ { "id": 1918946, "author_name": "jiweiliu", "author_url": "", "post_date": "08/30/2022 02:09:36", "content": "These links for the last year competition could also be helpful:
\nhttps://openproblems.bio/neurips_docs/data/about_multimodal/
\nhttps://openproblems.bio/neurips_docs/about_tasks/task1_modality_prediction/
\nhttps://youtu.be/Fm-MDpPo85c
\nhttps://youtu.be/ZXDILOyiy7A
Thanks @jiweiliu 😊!
", "votes": null, "replies": [] }, { "id": 1920057, "author_name": "gookler", "author_url": "", "post_date": "08/30/2022 20:41:53", "content": "Thank you!
", "votes": null, "replies": [] }, { "id": 1920953, "author_name": "aliebhmi", "author_url": "", "post_date": "08/31/2022 13:44:00", "content": "This is great.
", "votes": null, "replies": [] }, { "id": 1921290, "author_name": "uchanmaigbokwe", "author_url": "", "post_date": "08/31/2022 17:29:43", "content": "The information in these links have been insightful. Thank you.
", "votes": null, "replies": [] }, { "id": 1921915, "author_name": "lilgaussy", "author_url": "", "post_date": "09/01/2022 06:32:23", "content": "@jiweiliu Just as a sanity check, here Multiome and CITEseq corresponds to the ATAC->GEX and GEX->ADT subtasks respectively in the 2021 NeurIPS competition?
", "votes": null, "replies": [ { "id": 1922656, "author_name": "jiweiliu", "author_url": "", "post_date": "09/01/2022 16:07:51", "content": "yes, exactly.
", "votes": null, "replies": [] }, { "id": 1923013, "author_name": "lilgaussy", "author_url": "", "post_date": "09/01/2022 23:08:56", "content": "Thanks, and congrats on Amex, autogressive features are such a cool idea.
", "votes": null, "replies": [] }, { "id": 1923098, "author_name": "jiweiliu", "author_url": "", "post_date": "09/02/2022 02:19:39", "content": "Thank you :)
", "votes": null, "replies": [] } ] }, { "id": 1923097, "author_name": "jsmithperera", "author_url": "", "post_date": "09/02/2022 02:17:38", "content": "Very useful info. Thanks!!!
", "votes": null, "replies": [] }, { "id": 1923260, "author_name": "shivanipopli", "author_url": "", "post_date": "09/02/2022 05:15:05", "content": "Thanks for sharing this @jiweiliu
", "votes": null, "replies": [] }, { "id": 1924609, "author_name": "supasunkumpraphan", "author_url": "", "post_date": "09/03/2022 08:18:41", "content": "Thank you so much. It is very useful.
", "votes": null, "replies": [] }, { "id": 1925882, "author_name": "digitalbro", "author_url": "", "post_date": "09/04/2022 12:14:36", "content": "Thanks, these are great resources!
", "votes": null, "replies": [] }, { "id": 1926285, "author_name": "alexandervc", "author_url": "", "post_date": "09/04/2022 17:48:43", "content": "https://youtu.be/i6s38LGKtX4
\nKsenia Petrova gives overview of some solutions of the previous year competition.
\nPS
\nFor the other talks:
\nsee https://www.kaggle.com/competitions/open-problems-multimodal/discussion/348661
Thanks for your sharing!
", "votes": null, "replies": [] }, { "id": 1930779, "author_name": "chrisrichardmiles", "author_url": "", "post_date": "09/08/2022 07:25:07", "content": "To any newbies like me, I suggest starting with the competition summary paper. I believe this competition is the same as Task 1 of the 2021 competition, so you could just focus on that part.
", "votes": null, "replies": [] }, { "id": 2028538, "author_name": "", "author_url": "", "post_date": "11/14/2022 03:25:29", "content": "thanks for this!
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1918930": "As you might already know, this competition was hosted in 2021 NeurIPS as well. Winners presented their solutions in the workshop remotely. You can find the recording here:\n\nhttps://drive.google.com/file/d/1aQss-KyfYlzdrBQcH5joiXMlTwpG5gdf/view?usp=sharing\n\nSource code of winning solutions can be found here:\nhttps://github.com/openproblems-bio/neurips2021_multimodal_topmethods", "1918946": "These links for the last year competition could also be helpful:\nhttps://openproblems.bio/neurips_docs/data/about_multimodal/\nhttps://openproblems.bio/neurips_docs/about_tasks/task1_modality_prediction/\nhttps://youtu.be/Fm-MDpPo85c\nhttps://youtu.be/ZXDILOyiy7A", "1919013": "Thanks @jiweiliu 😊!", "1920057": "Thank you!", "1920953": "This is great.", "1921290": "The information in these links have been insightful. Thank you.", "1921915": "jiweiliu Just as a sanity check, here Multiome and CITEseq corresponds to the ATAC->GEX and GEX->ADT subtasks respectively in the 2021 NeurIPS competition?", "1922656": "yes, exactly.", "1923013": "Thanks, and congrats on Amex, autogressive features are such a cool idea.", "1923097": "Very useful info. Thanks!!!", "1923098": "Thank you :)", "1923260": "Thanks for sharing this @jiweiliu", "1924609": "Thank you so much. It is very useful.", "1925882": "Thanks, these are great resources!", "1926285": "https://youtu.be/i6s38LGKtX4\nKsenia Petrova gives overview of some solutions of the previous year competition. \nPS\nFor the other talks:\nsee https://www.kaggle.com/competitions/open-problems-multimodal/discussion/348661", "1929576": "Thanks for your sharing!", "1930779": "To any newbies like me, I suggest starting with the competition summary [paper](https://proceedings.mlr.press/v176/lance22a.html). I believe this competition is the same as Task 1 of the 2021 competition, so you could just focus on that part.", "2028538": "thanks for this!" }, "source": "meta" }