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]
