Hello everybody!

I propose you a survey part of a research project at the University of Pisa.

The results of this questionnaire can be useful to everyone, providing a benchmark on their work. Topic of the questionnaire: data science process activities, risk, characteristic and data scientist approach to design.

Spending 5 minutes of your time you will be rewarded with a selection of 10 scientific articles from our database, based on your answers.

link to the questionnaire —> https://forms.gle/ZKMeGBZXA3hFZyf88

Furthermore, the results of this survey will be posted here, in order to share a benchmark overview of how data scientists work.

Here responses sheet in real time—> https://drive.google.com/drive/folders/13QDBwDvlT2MXQ2oiOHdJU23eylNW5Kfx?usp=sharing

You will receive the papers within a week, leaving the email in the questionnaire.

If you prefer, you can fill it in a completely anonymous form.

We will send you a notification for the data analysis report in February 2020.

E-mails will be sent from [designingdatascience@gmail.com](mailto:designingdatascience@gmail.com)

many think the Turing award committee made a mistake in 2019, even the big reddit post Hinton, LeCun, Bengio receive ACM Turing Award (680 upvotes) was mostly about Jurgen

a while ago there was a fun post We find it extremely unfair that Schmidhuber did not get the Turing award. That is why we dedicate this song to Juergen to cheer him up. but I bet the 2020 award would cheer him up even more, maybe it’s just that nobody nominated him in 2019, probably one has to be well-connected for that, some call him an outsider, but perhaps we can have some sort of grass-roots movement, someone should nominate him for the 2020 Turing award, I cannot do it myself, not senior enough, the nominator must be a “recognized member of the community,” it may help to have more than one nominator, here the nomination form and CV and publications: Next Deadline January 15, 2020 – End of Day, Anywhere on Earth (AoE), UTC -12 hrs

they also want supporting letters from at least 4, and not more than 8, endorsers, they should be well-known researchers, many of them are here on reddit and might read this, for example, Yoshua replied to a recent post, although something tells me he won’t write a supporting letter, but I hope your colleagues will

to find material for this, look at Jurgen’s very dense blog post on their annus mirabilis 1990-1991 with Sepp Hochreiter and other students, this overview has many original references and additional links, also on what happened in the decades after 1991 and its impact on industry and the world, I learned a lot from it and made digestible chunks for several reddit posts, with many supportive comments:

1. Jurgen Schmidhuber really had GANs in 1990 (560 upvotes), he did not call it GAN, he called it curiosity, it’s actually famous work, GANs are a simple application thereof, GANs were mentioned in the Turing laudation, it’s both funny and sad that Yoshua got a Turing award for a principle that Jurgen invented decades before him

2. DanNet, the CUDA CNN of Dan Ciresan in Jurgen Schmidhuber’s team, won 4 image recognition challenges prior to AlexNet (280), DanNet won ICDAR 2011 Chinese handwriting, IJCNN 2011 traffic signs, ISBI 2012 brain segmentation, ICPR 2012 cancer detection, DanNet was the first superhuman CNN in 2011

3. Five major deep learning papers by Geoff Hinton did not cite similar earlier work by Jurgen Schmidhuber (490): First Very Deep NNs, Based on Unsupervised Pre-Training (1991), Compressing / Distilling one Neural Net into Another (1991), Learning Sequential Attention with NNs (1990), Hierarchical Reinforcement Learning (1990), Geoff was editor of Jurgen’s 1990 paper, later he published closely related work, but he did not cite

of course, don’t take my word for it, when unsure, follow the links to the original references and study them, that’s what I did, that’s what made me sure about this

unlike Geoff & Yoshua & Yann, Jurgen also credits the pioneers who came long before him, as evident from the following posts:

1. Jurgen Schmidhuber on Seppo Linnainmaa, inventor of backpropagation in 1970 (250), the recent Turing award laudation refers to Yann’s variants of backpropagation and Geoff’s computational experiments with backpropagation, without clarifying that the method was invented by others, this post got a reddit gold award, thanks a lot for that!

2. Jurgen Schmidhuber on Alexey Ivakhnenko, godfather of deep learning 1965 (100), Ivakhnenko started deep learning before the first Turing award was created, but he passed away in 2007, one cannot nominate him any longer

the following posts refer to earlier posts of mine, thanks for that:

1. NeurIPS 2019 Bengio Schmidhuber Meta-Learning Fiasco (530), this shows that Jurgen had meta-learning first in 1987, long before Yoshua

2. The 1997 LSTM paper by Hochreiter & Schmidhuber has become the most cited deep learning research paper of the 20th century (410), this was about counting citations, LSTM has passed the backpropagation papers by Rumelhart et al. (1985, 1986, 1987) and also the most cited paper by Yann and Yoshua (1998) which is about CNNs, Jurgen also calls Sepp’s 1991 thesis “one of the most important documents in the history of machine learning” in The Blog, btw one should also nominate Seppo and Sepp, both highly deserving

Jurgen can be very charming, like in this youtube video of a talk in London “let’s look ahead to a time when the universe is going to be a thousand times older than it is now,” probably irrelevant for this award, but cool, and here is the video of a recent short talk at NeurIPS 2019 on his GANs of 1990 starting at 1:30, there are additional videos with many more clicks

if you are in a position to nominate, or you know someone who is, why not try this, I know, professors have little time, and there are only two weeks left until the deadline, but Jurgen and his students have done so much for our field

What’s your background? Which areas do you read in? What fraction is relevant to your ongoing projects?

I use full transformer (encoder-decoder) for sequence labeling .

During the training whole transformer for NER task process:

encoder source word sequence input: “Lily goes to company”

decoder target tag sequence input:’ Person O O Location ‘

If the source vocabulary have 5272 words , If the target vacabulary have 6 tag.

should encoder input embedding size must equal to decoder input embdding size.

for example:

encoder sequence: “Lily goes to company”

target sequence: ‘ Person O O Location ‘

encoder input embedding should map shape [4,5272] to [4,512]

Should decoder input embedding must map shape [4,6] to [4,512]???

I go to a small but fairly well-respected school and we have good students and good faculty, but none of the computer science profs specialize in deep learning. I have a project I’m working on right now with the intent to publish, and I’m starting on my masters thesis next semester (I’m a BS/MS student in my 4th year) and I’d really like to do a deep learning topic, maybe something related to this project I’m working on right now, but with no one to properly advise me I’m not sure what to do. I’m afraid to even publish this work I’ve been doing independently because I feel like I should run it by someone who isn’t an undergrad before I put it out into the world.

go to a different school lol

I mean I could but I would probably have to retake all the graduate courses I’ve taken, so I’m looking at a couple of years and a lot more debt before I have a Masters.

One option is that we have a very well respected medical/BME program that is doing a lot of stuff with deep learning, but I don’t know how much of it is actually moving the field of ML forward. Does anyone have any advice?

I am a ML engineer in a not super known company. I have a MS. I have aspirations to join a more research-focused team. What is the path to a genius level understanding of ML?

I think I sort of outgrew ML courses. I read research papers on a weekly basis. I always have side projects going on. There are some books I see recommended, The Elements of Statistical Learning , Deep Learning with Python and Machine Learning: a Probabilistic Perspective. I wonder if books like these are the next step for me.

Let’s say I have a pretrained VAE, If I always decode the mean vector instead of sampling from N(mean,std^2), does it mean the VAE turned into a regular deterministic AE?