Hi everyone,

I guess everyone here works or has worked as a Machine Learning Engineer (or similar). I think the definition of this role depends on the company you are working: it might be already using and deploying ML models, implementing and training some specific ML systems, reading and implementing the models from the papers, working on a library and so on.

So, my question is, how is your work organized, what does it include as a Machine Learning engineer?

Hello everyone,

I was wondering if anyone else have similar feelings with regards to a number of accepted papers coming from Chinese universities/authors presented in ICCV. Thus far in the conference, I came across quite a lot of papers with questionable motives which made me question the ethical consequences.

These papers are, for the most part, concerned with various forms of person identification (i.e., typical big brother stuff). In fact, when you look at the accepted papers, more than 80% of any kind of identification papers have Chinese authors/affiliations.

But that’s not all, some papers go to extreme lengths of person re-identification such as:

1- Occluded person re- identification (i.e., person re-identification through mask/glass)

2- Person re-identification in low-light environments

3- Cross domain person re-identification

4- Cross dataset person re-identification

5- Cross modality person re-identification

6- Unsupervised person re-identification

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And maybe you think person re-identification is all there is, but its not. There are also:

1- Vehicle identification, vehicle re-identification, vehicle re-identification from aerial images

2- Occluded vehicle recovery

3- Lip reading from video sequences

4- Crowd counting in scenes, crowd density prediction, and crowd counting in aerial pictures (in fact, all but one crowd counting papers are China affiliated)

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I wonder whether I am being overly sensitive due to recent influx of news about Uighurs in China and Hong Kong protests etc. or if these papers are basically funded by the Chinese government (or its extensions) for some big brother stuff.

What is your opinion on the research on these subjects which can be used for some ethically questionable applications getting published in top conferences?

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Edit: I should mention that I did not mean to offend any Chinese researchers and I am of course aware that many great inventions in recent ML/DL research that we use came from Chinese researchers. What I stated above is merely my observation while passing by the posters in the conference.

I was planning to learn handcrafting features by referring kaggle notebooks and some other resources .

We are really lucky that all the best players in AI (google, openAI, fb, etc) are all contributing their insights to the public, aren’t we? Even if some may not be open source, they all share their methods in detail.

Could you imagine a world where all the best players hid their techniques? I guess we would be years behind by now.

I am very interested in what is happening at the crossroads between computer science and art, especially algorithmic and ML generated / supported art (music, film, paintings, installations, etc.).

By this, I don’t mean using ML to develop tools that are used in the creative process (improving a 3D modeling software using ML for instance), but using Machine Learning as the creative tool in itself (a standard example being NextRembrandt).

My background is in ML and CS and I have been toying with the idea of pursuing a job / PhD in a related project.

Can anyone point me to any research labs / institutions / projects that they find interesting, fit the description and could maybe accept applications?

I know it is a pretty broad question. I’m looking for opinions from those of you who already know an interesting project that maybe hasn’t had much public attention yet.

Any help would be great! Thank you.

(for those of you without suggestions, but interested in the topic, I leave you this project I found the other day).

Asking for a friend.

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In terms of Ghz/Gb/time/etc.

Interesting paper published in Nature Neuroscience discusses ways where the neuroscience community might benefit from thinking like deep learning people.

Abstract

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Paper: https://www.nature.com/articles/s41593-019-0520-2 (anyone have link to full pdf)?

Summary by the author: https://threadreaderapp.com/thread/1188868863850500096.html