Hello,

I was planning of publishing a paper on a special issue of Sensors (MDPI) that seems to be strongly related to my work. After having a better look I’ve found hundreds of other special issues, all with similar deadlines and on very similar topics. Also, this journal seems to have a very rapid publication process, with “first decision provided to authors approximately 19.8 days after submission“. I’ve read some pretty bad things in the past about MDPI, but I always thought that Sensors was a serious journal, and I often find interesting research on it. So now I’m really confused. What do you think about MDPI Sensors? Is it worth publishing there, or would it be a waste?

Thank you.

I have a pet project built with the Computer Vision service from Microsoft. Sometimes it provides very accurate annotations and descriptions like ‘A view of a snow covered mountain’ (confidence 0.97) for an image of a mountain but mostly it’s utter garbage like ‘A motorcycle is parked on the side of a road’ (confidence 0.8) for a Formula 1 car.

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The Vision AI service from Google is doing even worse.

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I’m not seeing any significant improvements in this field at all. You can get a very realistic image of older you, but no one is able to annotate even a simple photo yet.

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Do you think we will have truly working Computer Vision within next few years?

I’m looking to find resources on applications of Transformers to anything else than NLP.

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I’m especially interested in finding papers that apply Transformers to State Representation Learning and Reinforcement Learning.

I am training a neural network using batches of soft labels, e.g.

y = [[0.00, 0.25, 0.25, 0.50], ... [0.75, 0.00, 0.20, 0.05]] 

However, as opposed to one-hot labels, if the softmax activation function outputs a list ŷ equal to y (no loss), as in

y = ŷ = [0.00, 0.25, 0.25, 0.50] 

the cross-entropy function is not 0:

loss = -sum(y * log(ŷ)) = 1.0397 

although it is true that with no other ŷ we can reach a lower value, given y.

Then, the more sparse y is, the larger is the minimum possible loss:

y = ŷ = [0.25, 0.25, 0.25, 0.25] loss = -sum(y * log(ŷ)) = 1.3862 

So my question is, would this lower bound in the minimum possible loss constitute a bias when training/testing a neural network? Since a neural network yields a higher minimum cost for more sparse soft labels than for less sparse (up to one-hot) labels, maybe the network adjusts the weights and biases towards a way to minimize the more sparse soft labels, in detriment of the less sparse soft and one-hot labels?

Saw this link on Yann Lecun’s facebook to Google Scholar Top Publications rankings.

ML/CV publication venues are moving up in the h5-index ranking:

10: CVPR (240) ahead of PNAS, PRL, JAMA.

27: NeurIPS (169)

42: ICLR (150) ahead of Neuron and Nature Neuroscience

56: ECCV (137)

59: ICML (135)

71: ICCV (129)

It is interesting that ICLR is higher ranked now compared to ICML. Also the other AI conferences with low acceptance rates, such as AAAI and IJCAI are not on this list. Top NLP conferences like ACL or EMNLP are also not on the list although IMO this might change in the next few years given the progress (or perceived progress 🙂 made from 2018.

Nowhere to be seen are ML / AI journals, so this list might be of some use for faculty members to justify publishing stuff to conferences when their legacy university departments demand journal publications.

https://scholar.google.com/citations?view_op=top_venues

http://arxiv.org/pdf/1907.08653v1 (pdf warning)

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Very interesting idea, but unfortunately only tests on Fashion MNIST