![\"enter](\"https://3.bp.blogspot.com/-U6Eu-1CEvXE/W9dEhAWddwI/AAAAAAAADeI/4crG3Z36pngB5_W75p_YCi6n9_h9fbZMgCLcBGAs/s640/f3v2.png\")
\nhttps://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.htmlIntroducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
\n\n... But as computational power and specialized deep learning hardware such as TPUs become more readily available, machine learning models will grow larger and ensembles will become more prominent. Now, imagine a tool that automatically searches over neural architectures, and learns to combine the best ones into a high-quality model.
\n\nAdaNet is easy to use, and creates high-quality models, saving ML practitioners the time normally spent selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks. AdaNet is capable of adding subnetworks of different depths and widths to create a diverse ensemble, and trade off performance improvement with the number of parameters.
\n\n\nhttps://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html
https://icml.cc/Conferences/2017/Videos
\n\n![\"enter](\"https://2.bp.blogspot.com/-MXSy_I9M6nI/W9cx1LsFKRI/AAAAAAAADdc/HSFi3QnzgNwv5ovScFkLKUT9vyhAqVu2QCLcBGAs/s400/image1.gif\")
Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
\n\n... But as computational power and specialized deep learning hardware such as TPUs become more readily available, machine learning models will grow larger and ensembles will become more prominent. Now, imagine a tool that automatically searches over neural architectures, and learns to combine the best ones into a high-quality model.
\n\nAdaNet is easy to use, and creates high-quality models, saving ML practitioners the time normally spent selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks. AdaNet is capable of adding subnetworks of different depths and widths to create a diverse ensemble, and trade off performance improvement with the number of parameters.
\n\n\nhttps://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html
https://icml.cc/Conferences/2017/Videos
\n\n
I saw the news recently. It is available only on Tensorflow, right?
", "rawMarkdown": "I saw the news recently. It is available only on Tensorflow, right?", "votes": null }, { "id": "414854", "postDate": "11/03/2018 18:31:24", "content": "I used Nasnet-A mobile in PyTorch. Check https://www.kaggle.com/iafoss/pytorch-pretrained-models#nasnet_a_mobile.pth and https://github.com/wandering007/nasnet-pytorch
", "rawMarkdown": "I used Nasnet-A mobile in PyTorch. Check https://www.kaggle.com/iafoss/pytorch-pretrained-models#nasnet_a_mobile.pth and https://github.com/wandering007/nasnet-pytorch", "votes": null }, { "id": "415013", "postDate": "11/04/2018 06:02:23", "content": "I would like to try this~
", "rawMarkdown": "I would like to try this~", "votes": null } ], "comments": [ { "id": 414764, "author_name": "kokecacao", "author_url": "", "post_date": "11/03/2018 14:51:39", "content": "I saw the news recently. It is available only on Tensorflow, right?
", "votes": null, "replies": [ { "id": 414854, "author_name": "iafoss", "author_url": "", "post_date": "11/03/2018 18:31:24", "content": "I used Nasnet-A mobile in PyTorch. Check https://www.kaggle.com/iafoss/pytorch-pretrained-models#nasnet_a_mobile.pth and https://github.com/wandering007/nasnet-pytorch
", "votes": null, "replies": [] } ] }, { "id": 415013, "author_name": "the16throute", "author_url": "", "post_date": "11/04/2018 06:02:23", "content": "I would like to try this~
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "413510": "Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees\n\n... But as computational power and specialized deep learning hardware such as TPUs become more readily available, machine learning models will grow larger and ensembles will become more prominent. Now, imagine a tool that automatically searches over neural architectures, and learns to combine the best ones into a high-quality model. \n\n\nAdaNet is easy to use, and creates high-quality models, saving ML practitioners the time normally spent selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks. AdaNet is capable of adding subnetworks of different depths and widths to create a diverse ensemble, and trade off performance improvement with the number of parameters.\n\n ![enter image description here][1]\nhttps://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html\n\nhttps://icml.cc/Conferences/2017/Videos\n\n ![enter image description here][2]\n\n\n [1]: https://3.bp.blogspot.com/-U6Eu-1CEvXE/W9dEhAWddwI/AAAAAAAADeI/4crG3Z36pngB5_W75p_YCi6n9_h9fbZMgCLcBGAs/s640/f3v2.png\n [2]: https://2.bp.blogspot.com/-MXSy_I9M6nI/W9cx1LsFKRI/AAAAAAAADdc/HSFi3QnzgNwv5ovScFkLKUT9vyhAqVu2QCLcBGAs/s400/image1.gif", "414764": "I saw the news recently. It is available only on Tensorflow, right?", "414854": "I used Nasnet-A mobile in PyTorch. Check https://www.kaggle.com/iafoss/pytorch-pretrained-models#nasnet_a_mobile.pth and https://github.com/wandering007/nasnet-pytorch", "415013": "I would like to try this~" }, "source": "meta" }