
15:46
Lasse Hansen: lassehansen.me

15:56
roberta rocca: https://rbroc.github.io/

27:56
Are these results for combining audio and text?

29:40
Thank you!

30:51
Are these prior results using the same dataset?

31:41
(I have lost my voice with laryngitis so I can’t unmute and interrupt…)

33:15
(sorry to hear!)

34:04
Thank you!

34:56
Wait, actually, I meant the “prior work” slide with results previously reported by other groups? Maybe three slides ago?

36:03
Yes!

36:22
Okay, got it!

36:23
I think it’s this meta-analysis from their group: https://pubmed.ncbi.nlm.nih.gov/31839552/

40:16
and this one for ASD also from their group: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aur.1678?casa_token=c5cmcmmM7eUAAAAA:puxXjIgyciQ590Vu-W_Om2Osk6mMjrjM7OrqXv0_LcoLZ5c7qxsJSpa24DenfsQDCxd2FYXWLXjhmw

40:26
Thanks, Daniel!

48:06
Don't talk!

48:07
:)

48:31
What sort of diagnostic info do you have for the ASD group? Is it ADOS?

49:35
Do you have info about their language abilities, which are differentially impact on language? Like do yuo know if they have a language impairment as well?

49:45
It has a lot of typos!

50:01
Thanks!

50:30
your next satra!

50:32
🙂

57:34
I had a similar question about evaluation. Have you considered “hold one out” cross validation? For autism, I find this really helpful since they are such a heterogenous group, but of course, you are exploring a lot of different models.

59:02
Thank you for the great talk!

01:00:40
Thank you!

01:00:45
We've been learning that k-fold nested cross-validation provides the best statistical power

01:01:58
our postdoc Hamzeh has been putting together a manuscript to compare nested CV with traditional train/test. This work also results in the need for more data samples.

01:02:21
Thank you for a great talk!

01:06:41
Nicholas Cummins has a great paper looking into symptom-level analysis of depression: https://arxiv.org/pdf/2204.00088

01:06:54
Congrats!