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Posted to events@mxnet.apache.org by "Robinson, Danielle" <dm...@amazon.com.INVALID> on 2020/11/21 00:03:11 UTC
GluonTS Presentation Abstract
Hello,
I am including my abstract for my talk on GluonTS on MXNet Day.
“We present the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms. “
Thank you,
Danielle Robinson
Re: GluonTS Presentation Abstract
Posted by "Robinson, Danielle" <dm...@amazon.com.INVALID>.
Hi Vartikas,
Here is the mp4 of my GluonTS video https://drive.google.com/file/d/1eAfVMSOtyDC1UmNuC4ecuMNT6tUvpPIt/view?usp=sharing. Let me know if you need anything else and if there are any issues. Looking forward to the Slack discussion channel as well.
Thank you,
Danielle
On 12/10/20, 12:40 PM, "Sheng Zha" <sz...@gmail.com> wrote:
CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe.
+Danielle
On Thu, Dec 10, 2020 at 2:07 AM Vartika Singh <va...@gmail.com> wrote:
>
> Hello Danielle,
>
> 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 you 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 2020/11/21 00:03:11, "Robinson, Danielle" <dm...@amazon.com.INVALID> wrote:
> > Hello,
> >
> > I am including my abstract for my talk on GluonTS on MXNet Day.
> >
> > “We present the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms. “
> >
> > Thank you,
> >
> > Danielle Robinson
> >
> >
>
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Re: GluonTS Presentation Abstract
Posted by "Robinson, Danielle" <dm...@amazon.com.INVALID>.
Hi Sheng and Vartika,
Yes, I can send you a link of the video recording by tomorrow. Which would be the best platform to share this?
Here is a video of my GluonTS talk from the Time Series Workshop at ICML: https://slideslive.com/38917683/neural-time-series-models-with-gluonts. I'm not sure if this would be okay to use. Otherwise, let me know and I can record.
Thanks!
Danielle
On 12/10/20, 12:40 PM, "Sheng Zha" <sz...@gmail.com> wrote:
CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe.
+Danielle
On Thu, Dec 10, 2020 at 2:07 AM Vartika Singh <va...@gmail.com> wrote:
>
> Hello Danielle,
>
> 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 you 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 2020/11/21 00:03:11, "Robinson, Danielle" <dm...@amazon.com.INVALID> wrote:
> > Hello,
> >
> > I am including my abstract for my talk on GluonTS on MXNet Day.
> >
> > “We present the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms. “
> >
> > Thank you,
> >
> > Danielle Robinson
> >
> >
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: events-unsubscribe@mxnet.apache.org
> For additional commands, e-mail: events-help@mxnet.apache.org
>
---------------------------------------------------------------------
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Re: GluonTS Presentation Abstract
Posted by Sheng Zha <sz...@gmail.com>.
+Danielle
On Thu, Dec 10, 2020 at 2:07 AM Vartika Singh <va...@gmail.com> wrote:
>
> Hello Danielle,
>
> 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 you 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 2020/11/21 00:03:11, "Robinson, Danielle" <dm...@amazon.com.INVALID> wrote:
> > Hello,
> >
> > I am including my abstract for my talk on GluonTS on MXNet Day.
> >
> > “We present the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms. “
> >
> > Thank you,
> >
> > Danielle Robinson
> >
> >
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: events-unsubscribe@mxnet.apache.org
> For additional commands, e-mail: events-help@mxnet.apache.org
>
---------------------------------------------------------------------
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For additional commands, e-mail: events-help@mxnet.apache.org
Re: GluonTS Presentation Abstract
Posted by Vartika Singh <va...@gmail.com>.
Hello Danielle,
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 you 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 2020/11/21 00:03:11, "Robinson, Danielle" <dm...@amazon.com.INVALID> wrote:
> Hello,
>
> I am including my abstract for my talk on GluonTS on MXNet Day.
>
> “We present the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms. “
>
> Thank you,
>
> Danielle Robinson
>
>
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