TL;DR: Thank you all for you hard work, we thoroughly enjoyed the experience and I hope it has been a great learning experience for you as well. If you have time - please fill in our exit survey: https://forms.gle/ZpWH7EUoVDyfkgUX6
\nObserving the competition unfold has been one of the most intense and rewarding experiences. As first-time organizers on the Kaggle platform, we are delighted by the overwhelming positive response from the community. Now that we've reached the end, we'd like to share our perspective from the organizer's side of the fence.
\nWe acknowledge that this competition was particularly challenging. The small dataset, unforgiving metric, and distribution shift weren't designed to make things needlessly difficult - they reflect both Ariel's expected yield and our commitment to uncertainty estimation. One of our primary objectives was to evaluate whether deep learning approaches are suitable for this task, or if traditional data science methods remain more appropriate. The answer, naturally, isn't binary, and we'll be analyzing your solutions to draw conclusions. (Should this lead to a publication, we may reach out to some of you as potential co-authors, though we can't make any guarantees yet!)
\nYour data science expertise and ability to grasp the subject matter have truly impressed us. The domain knowledge required was substantial, yet within hours of launch, we saw posts about previous ADCs, relevant literature, and problem illustrations that sometimes exceeded our own explanations! Our only regret is not being able to address all your questions comprehensively. Our team frequently debated between providing more information and maintaining discretion to prevent potential leaks. Given recent incidents in other competitions, we opted for a conservative approach - we apologize if this seemed overly cautious.
\nWe must express our profound gratitude to our Kaggle team members, @sohier and @maggiemd. Their contribution was instrumental in making this competition a remarkable success. Following dataset leakage issues in other competitions, the Kaggle team took extensive precautions, working closely with us during preparation to ensure maximum security. While no competition is entirely perfect, we want to acknowledge the behind-the-scenes efforts of the Kaggle team in ensuring fair competition.
\nIn essence, THANK YOU! We've thoroughly enjoyed this journey and hope you've gained new insights about exoplanets, Ariel, and the application of DS/ML in astronomy.
\nIf you could spare 10 minutes to complete our exit survey ( https://forms.gle/ZpWH7EUoVDyfkgUX6 ), your feedback would be invaluable in helping us design better competitions in the future.
\nBest regards,
\nThe Ariel Data Challenge 2024 Organizing Team
", "messages": [ { "id": 3037205, "postDate": "2024-11-05T13:35:10.917Z", "content": "TL;DR: Thank you all for you hard work, we thoroughly enjoyed the experience and I hope it has been a great learning experience for you as well. If you have time - please fill in our exit survey: https://forms.gle/ZpWH7EUoVDyfkgUX6
\nObserving the competition unfold has been one of the most intense and rewarding experiences. As first-time organizers on the Kaggle platform, we are delighted by the overwhelming positive response from the community. Now that we've reached the end, we'd like to share our perspective from the organizer's side of the fence.
\nWe acknowledge that this competition was particularly challenging. The small dataset, unforgiving metric, and distribution shift weren't designed to make things needlessly difficult - they reflect both Ariel's expected yield and our commitment to uncertainty estimation. One of our primary objectives was to evaluate whether deep learning approaches are suitable for this task, or if traditional data science methods remain more appropriate. The answer, naturally, isn't binary, and we'll be analyzing your solutions to draw conclusions. (Should this lead to a publication, we may reach out to some of you as potential co-authors, though we can't make any guarantees yet!)
\nYour data science expertise and ability to grasp the subject matter have truly impressed us. The domain knowledge required was substantial, yet within hours of launch, we saw posts about previous ADCs, relevant literature, and problem illustrations that sometimes exceeded our own explanations! Our only regret is not being able to address all your questions comprehensively. Our team frequently debated between providing more information and maintaining discretion to prevent potential leaks. Given recent incidents in other competitions, we opted for a conservative approach - we apologize if this seemed overly cautious.
\nWe must express our profound gratitude to our Kaggle team members, @sohier and @maggiemd. Their contribution was instrumental in making this competition a remarkable success. Following dataset leakage issues in other competitions, the Kaggle team took extensive precautions, working closely with us during preparation to ensure maximum security. While no competition is entirely perfect, we want to acknowledge the behind-the-scenes efforts of the Kaggle team in ensuring fair competition.
\nIn essence, THANK YOU! We've thoroughly enjoyed this journey and hope you've gained new insights about exoplanets, Ariel, and the application of DS/ML in astronomy.
\nIf you could spare 10 minutes to complete our exit survey ( https://forms.gle/ZpWH7EUoVDyfkgUX6 ), your feedback would be invaluable in helping us design better competitions in the future.
\nBest regards,
\nThe Ariel Data Challenge 2024 Organizing Team
", "rawMarkdown": "TL;DR: Thank you all for you hard work, we thoroughly enjoyed the experience and I hope it has been a great learning experience for you as well. If you have time - please fill in our exit survey: https://forms.gle/ZpWH7EUoVDyfkgUX6\n \n------------------------------------------------------------\n \nObserving the competition unfold has been one of the most intense and rewarding experiences. As first-time organizers on the Kaggle platform, we are delighted by the overwhelming positive response from the community. Now that we've reached the end, we'd like to share our perspective from the organizer's side of the fence.\n \nWe acknowledge that this competition was particularly challenging. The small dataset, unforgiving metric, and distribution shift weren't designed to make things needlessly difficult - they reflect both Ariel's expected yield and our commitment to uncertainty estimation. One of our primary objectives was to evaluate whether deep learning approaches are suitable for this task, or if traditional data science methods remain more appropriate. The answer, naturally, isn't binary, and we'll be analyzing your solutions to draw conclusions. (Should this lead to a publication, we may reach out to some of you as potential co-authors, though we can't make any guarantees yet!)\n \nYour data science expertise and ability to grasp the subject matter have truly impressed us. The domain knowledge required was substantial, yet within hours of launch, we saw posts about previous ADCs, relevant literature, and problem illustrations that sometimes exceeded our own explanations! Our only regret is not being able to address all your questions comprehensively. Our team frequently debated between providing more information and maintaining discretion to prevent potential leaks. Given recent incidents in other competitions, we opted for a conservative approach - we apologize if this seemed overly cautious.\n \nWe must express our profound gratitude to our Kaggle team members, @sohier and @maggiemd. Their contribution was instrumental in making this competition a remarkable success. Following dataset leakage issues in other competitions, the Kaggle team took extensive precautions, working closely with us during preparation to ensure maximum security. While no competition is entirely perfect, we want to acknowledge the behind-the-scenes efforts of the Kaggle team in ensuring fair competition.\n \nIn essence, THANK YOU! We've thoroughly enjoyed this journey and hope you've gained new insights about exoplanets, Ariel, and the application of DS/ML in astronomy.\n \nIf you could spare 10 minutes to complete our exit survey ( https://forms.gle/ZpWH7EUoVDyfkgUX6 ), your feedback would be invaluable in helping us design better competitions in the future.\n \nBest regards, \n\nThe Ariel Data Challenge 2024 Organizing Team", "votes": 11 }, { "id": 3037469, "postDate": "2024-11-05T18:28:40.610Z", "content": "Thanks a lot for organising this competition, really well done!
\nThe only thing that really would have made it better in my view is if a randomly selected part of the test set (say 200 planets) had been included in the data, without ground truth included. This would in my view be more representative of the real mission, and would have saved us a lot of work that ultimately didn't lead to better moels.
", "rawMarkdown": "Thanks a lot for organising this competition, really well done! \n\nThe only thing that really would have made it better in my view is if a randomly selected part of the test set (say 200 planets) had been included in the data, without ground truth included. This would in my view be more representative of the real mission, and would have saved us a lot of work that ultimately didn't lead to better moels.", "votes": 1 }, { "id": 3041563, "postDate": "2024-11-10T14:14:46.380Z", "content": "Thank you very much for hosting this competition; you did very well!
", "rawMarkdown": "Thank you very much for hosting this competition; you did very well!" }, { "id": 3037792, "postDate": "2024-11-06T06:47:59.953Z", "content": "If your browser doesn't process the survey link correctly, use https://forms.gle/ZpWH7EUoVDyfkgUX6 (without parenthesis at the end).
", "rawMarkdown": "If your browser doesn't process the survey link correctly, use https://forms.gle/ZpWH7EUoVDyfkgUX6 (without parenthesis at the end).", "replies": [ { "id": 3037846, "postDate": "2024-11-06T08:42:03.950Z", "content": "Thank you 🤩
", "rawMarkdown": "Thank you 🤩" } ] }, { "id": 3038243, "postDate": "2024-11-06T18:37:59.657Z", "content": "Thanks for the competition!!
", "rawMarkdown": "Thanks for the competition!!" } ], "comments": [ { "id": 3037469, "author_name": "Jeroen Cottaar", "author_url": "", "post_date": "2024-11-05T18:28:40.610000", "content": "Thanks a lot for organising this competition, really well done!
\nThe only thing that really would have made it better in my view is if a randomly selected part of the test set (say 200 planets) had been included in the data, without ground truth included. This would in my view be more representative of the real mission, and would have saved us a lot of work that ultimately didn't lead to better moels.
", "votes": 1, "replies": [] }, { "id": 3041563, "author_name": "Humayra Khanom Rime", "author_url": "", "post_date": "2024-11-10T14:14:46.380000", "content": "Thank you very much for hosting this competition; you did very well!
", "votes": 0, "replies": [] }, { "id": 3037792, "author_name": "AmbrosM", "author_url": "", "post_date": "2024-11-06T06:47:59.953000", "content": "If your browser doesn't process the survey link correctly, use https://forms.gle/ZpWH7EUoVDyfkgUX6 (without parenthesis at the end).
", "votes": 0, "replies": [ { "id": 3037846, "author_name": "Gordon Yip", "author_url": "", "post_date": "2024-11-06T08:42:03.950000", "content": "Thank you 🤩
", "votes": 0, "replies": [] } ] }, { "id": 3038243, "author_name": "Data Warrior", "author_url": "", "post_date": "2024-11-06T18:37:59.657000", "content": "Thanks for the competition!!
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "3037205": "TL;DR: Thank you all for you hard work, we thoroughly enjoyed the experience and I hope it has been a great learning experience for you as well. If you have time - please fill in our exit survey: https://forms.gle/ZpWH7EUoVDyfkgUX6\n \n------------------------------------------------------------\n \nObserving the competition unfold has been one of the most intense and rewarding experiences. As first-time organizers on the Kaggle platform, we are delighted by the overwhelming positive response from the community. Now that we've reached the end, we'd like to share our perspective from the organizer's side of the fence.\n \nWe acknowledge that this competition was particularly challenging. The small dataset, unforgiving metric, and distribution shift weren't designed to make things needlessly difficult - they reflect both Ariel's expected yield and our commitment to uncertainty estimation. One of our primary objectives was to evaluate whether deep learning approaches are suitable for this task, or if traditional data science methods remain more appropriate. The answer, naturally, isn't binary, and we'll be analyzing your solutions to draw conclusions. (Should this lead to a publication, we may reach out to some of you as potential co-authors, though we can't make any guarantees yet!)\n \nYour data science expertise and ability to grasp the subject matter have truly impressed us. The domain knowledge required was substantial, yet within hours of launch, we saw posts about previous ADCs, relevant literature, and problem illustrations that sometimes exceeded our own explanations! Our only regret is not being able to address all your questions comprehensively. Our team frequently debated between providing more information and maintaining discretion to prevent potential leaks. Given recent incidents in other competitions, we opted for a conservative approach - we apologize if this seemed overly cautious.\n \nWe must express our profound gratitude to our Kaggle team members, @sohier and @maggiemd. Their contribution was instrumental in making this competition a remarkable success. Following dataset leakage issues in other competitions, the Kaggle team took extensive precautions, working closely with us during preparation to ensure maximum security. While no competition is entirely perfect, we want to acknowledge the behind-the-scenes efforts of the Kaggle team in ensuring fair competition.\n \nIn essence, THANK YOU! We've thoroughly enjoyed this journey and hope you've gained new insights about exoplanets, Ariel, and the application of DS/ML in astronomy.\n \nIf you could spare 10 minutes to complete our exit survey ( https://forms.gle/ZpWH7EUoVDyfkgUX6 ), your feedback would be invaluable in helping us design better competitions in the future.\n \nBest regards, \n\nThe Ariel Data Challenge 2024 Organizing Team", "3037469": "Thanks a lot for organising this competition, really well done! \n\nThe only thing that really would have made it better in my view is if a randomly selected part of the test set (say 200 planets) had been included in the data, without ground truth included. This would in my view be more representative of the real mission, and would have saved us a lot of work that ultimately didn't lead to better moels.", "3041563": "Thank you very much for hosting this competition; you did very well!", "3037792": "If your browser doesn't process the survey link correctly, use https://forms.gle/ZpWH7EUoVDyfkgUX6 (without parenthesis at the end).", "3038243": "Thanks for the competition!!" } }