#If the results cannot be reproduced from a dl paper

19 messages · Page 1 of 1 (latest)

knotty token
#

If you cannot reproduce the results from a dl paper, is it considered to be academic dishonesty from the author?

Im not reading the latest papers so some papers might be like 5 years ago. I think nowadays the larger the model the easier it is to reproduce the results? I dont really know since Im not an expert in deep learning and it's just my guess.

I dont really mean a specific paper, just a general question

west elm
#

@rough python @dim swift @unique ginkgo @pale tangle @weak sequoia@spice vine @inland cypress @olive bane@plush thistle What are your thoughts?

fervent sage
#

peer review in ML DL is usually super scuffed

#

i once had a reviewer rate me 3/5 on every criteria and quote part of my abstract verbatim as "review"

#

they often dont ask for code or data (unless u wanna specifically provide it to them)

#

missing a few lines of maths or some hyperparams really is considered no biggie

#

hey, found runoob here too lol

unique ginkgo
fervent sage
unique ginkgo
#

exactly

rough python
#

Things are changing. In other fields failure to reproduce a result is a scientific contribution on itself. We should get there in ML.

dim swift
#

Definitely a big problem as it is in fact not even expected that you provide code. Just an optional thing.
It's fine for very basic stuff, e.g. modifying relu for another activation function or something, but for a whole new mechanism then it should be required.

I also believe we will get there as DrDub said. Things are just slower than we want them to be haha.

fervent sage
rough python
fervent sage
unique ginkgo
rough python
unique ginkgo
#

it started in psychology, or at least psychology was the first one where it was broadly recognized as systemically problematic, and now even the 'hard' sciences are facing these issues consistently.

rough python