
01:34
Yes! I bumped off

04:51
yes I can see. Cannot hear you well

13:11
Você está controlando o tempo?

20:39
Please send the questions using the chat, as we will have a Q&A session at the end of the session.

23:00
Would love to hear more about your method Konstantin!

23:16
For Konstantin: how do you qualitatively evaluate your model in terms of how good it is in detecting outliers?

23:31
quantitively*

24:08
quantitatively**

24:35
can others hear?

24:46
yes

24:56
yes

26:54
For Paula: how can you get such a high accuracy in classifying SLSNe?

27:07
Really great everyone!

29:48
More questions, anyone?

30:06
To Ashley: how good is the classification of SuperRAENN? Have you compared it against other models in the literature?

30:46
Thanks Paula!

32:03
Thanks Konstantin and Ashley!

32:16
clap clap

41:42
It is much faster to train if all data is one size (just because you can do the matrix multiplications in one step)

41:59
But it doesn’t have to be one size. That is the short answer :)

51:59
Is there going to be a second run of the talks? I’d hopped over hoping to catch them, but maybe I was too late.

52:17
Things were pushed back 15 minutes - they are about to start

52:20
Yes — starting at 2:15

52:22
Yes, we will start in 2 minutes!

52:23
Ah, thanks

52:40
You're welcome!

55:07
Please submit your questions here, as we'll have a Q&A session at the end!

01:06:19
Are there any questions for any of the speakers?

01:08:33
If there’s time, I’d like to hear more about which features Ashley hand-selected for her photometric classifications.

01:09:13
I second Chris' question!

01:10:09
Spoiler for your question, Chris: They aren’t super interesting! lol

01:12:12
Ashley follow-up question, did you use redshift (photometric or otherwise) as a feature?

01:12:24
No, but LCs are corrected for redshift

01:12:32
ok thanks