+Jonas On Thu, Dec 10, 2020 at 2:05 AM Vartika Singh <[email protected]> wrote: > > Hello Mueller, > > Apologies for the late response. > > Our agenda is full. However, we were hoping that you could make your talk > available to us as a recording of 15-20 minutes and send a link to the > downloadable video by end of Friday? > > We will not be able to slot you in the agenda, however we can make the video > available for attendees to view. We will also create a slack channel > specifically for your talk where folks can ask questions to yoou directly. > > Would this be acceptable to you? If yes, please let us know and send the link > to video recording, mp4, by end of Friday. > > Warm Regards > Vartika > > On 2020/12/03 01:18:10, "Mueller, Jonas" <[email protected]> wrote: > > Hi I just wanted to follow-up and make sure this has been received? > > > > Thanks, > > Jonas > > > > From: "Mueller, Jonas" <[email protected]> > > Date: Friday, November 20, 2020 at 6:30 PM > > To: "[email protected]" <[email protected]> > > Cc: "Ye, Wen-ming" <[email protected]> > > Subject: Talk Abstract for MXNet day: AutoGluon > > > > Hi, I’m writing to submit an abstract for a proposed talk for Apache MXNet > > day. > > > > Thanks for your consideration! > > Jonas > > > > Talk Title: AutoGluon: Powerful and Easy-to-use AutoML for Text, Image, and > > Tabular Data > > > > Talk Abstract: > > > > AutoGluon<https://github.com/awslabs/autogluon/> is an open-source software > > toolkit to automate machine learning (ML) on image, text, and tabular data > > (built on top of MXNet). With a couple lines of Python code, anybody can > > translate their raw data into highly accurate predictions, regardless of > > their ML expertise. In comprehensive > > benchmarks<https://arxiv.org/pdf/2003.06505.pdf> over 50 datasets, > > AutoGluon produced significantly more accurate models (given the same > > runtime/compute) than other popular AutoML tools like TPOT, H2O, AutoWEKA, > > auto-sklearn, Google AutoML Tables. In two prominent Kaggle competitions > > with tabular data, AutoGluon outperformed 99% of human data science teams > > after just being run for 4 hours on the raw data. In comparisons with > > human data scientists on 4 image classification competitions from > > Kaggle<https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8>, > > AutoGluon consistently ranked around the top 10%. AutoGluon > has also been used to win a number of internal prediction competitions at > Amazon, and is being used in production for multiple applications. This talk > describes how AutoGluon achieves such strong predictive performance through > state-of-the-art deep learning, hyperparameter-optimization, stack > ensembling, and other modelling techniques. > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [email protected] > For additional commands, e-mail: [email protected] >
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