Hi all, profuse apologies in advance if this is not the correct place to ask this question. I’ve attempted to look around for information (both online and offline), but perhaps I’m not hitting the right keywords, so I though I’d give this a try.

My question is specifically about “quant researcher” type careers, and what the pros/cons and other considerations are when taking up a job like that.

My understanding is that post-PhD (in ML, or whatever the department is that accommodated your ML research for the bulk of the PhD), the majority of people aim for (1) “research scientist” roles in industry, or (2) focus more on an academic career, or (3) both, simultaneously. Obviously this is a generalization, and there are many more things you can do with any STEM PhD for that matter, but these options seem to be the goals of many people.

What about (4) quant jobs in finance, such as in small/large trading / hedge funds / asset management / etc.? They frequently appear to offer extremely attractive packages, and require no experience in finance. However, for the most part the community seems to be rather separate from the (1) through (3) crowd I mentioned above, so I am unable to get a coherent picture of why some folks choose one path versus another, and the various things you should consider (e.g. long-term career trajectory, exit options, etc.) when taking your first job / internship in finance during / after your PhD.

Apologies for the naive question, and apologies again if this is not the right place to ask this kind of thing. Thank you in advance for your kind advice!

paper: https://arxiv.org/pdf/1903.10873.pdf

code: https://github.com/apchenstu/Facial_Details_Synthesis

Hi there,

I recently built a new PC with an AMD RX5700 (with AMD Ryzen 3600 CPU) and was wondering if there are any popular frameworks for training on the GPU, preferably in Python. I cannot seem to find a concise answer on this. Any help/suggestions would be greatly appreciated. Thank you.

Is there anybody with neuroscience background who moved to ML research? Any ML researchers who deliberately decided to learn some neuroscience to get new ideas?

Would you say it was worth it to learn neuroscience for you? Would you say it would be better to just focus purely on ML?

I am finishing my CS undergrad and deciding between two options for grad school:

1) PhD in Optimization/ML theory

2) MS in Neuroinformatics (and then eventually going for PhD in ML theory)

I am generally interested in learning neuroscience and understanding how the brain works. However, it seems the theory is not quite here yet, and I do not want to work on experimental biological side. I ultimately want to work on ML theory as I think ML has the most impact here and now, while it will likely take some decades until neuroscience is sufficiently developed. That is why I am considering to learn some core neuroscience concepts, and then try to apply those concepts to find novel ideas for ML.

The Neuroinformatics MS program is quite flexible and will allow me to be primarily focused on ML and open-ended research, while 1/3 of my courses will be in neuroscience. I will work on bio-plausible backprop (some references are here) and maybe spiking neural networks. Somewhat unrelatedly, being there may give me some insight into brain-computer interfaces research while there is growing interest in that.

I think doing that MS will give me more diverse background and ideas for further research in ML theory, and open more doors. However, I am somewhat concerned if it is worth it, wouldn’t doing pure ML leave me in in a better position?

Also, I am a bit sceptical about bio-plausible ML research. While it is really interesting, it seems to be a bit of a “toy” problem. We don’t even know if something like backprop happens in the brain, so trying to make it more “bio-plausible” for its own sake is somewhat of an artificial problem.

There was a related discussion: [D] Computational Neuroscience and Machine Learning

Somewhat old paper, https://arxiv.org/abs/1506.04696. I recently spent some time going over this, and the paper has some great proofs and discussion. I did however,find myself looking at their theorem and thinking “how on earth did they find that form of the drift coefficient”, and found the proof to be mainly about showing if you use that form, the result holds. That’s not too enlightening if you want insight into how they found the result, so I went the other way myself, and in 1D it turns out to be somewhat straight-forward. I wrote it up if anybody else finds this view interesting

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https://chrisorm.github.io/SDE-S.html

I apologize if this post doesn’t quite match this subreddit, but I’m really looking for suggestions from active practitioners and researchers in the field and this looks like a great place to find one!

I’m a computer engineering student, about to begin my last year of bachelor’s degree. As you could imagine, the AI course I had to take was nothing but primitive search algorithms. But with the help of one of my professors, I was able to study ML and DL in particular, using a number of courses and a few books. Right now I’ve one paper almost ready for submission and a novel model on its way (both GAN-related).

Now I’m wondering about the subject of my thesis. I talked to my professor a while ago and he suggested to work on a model that could take in a face and output the same person at some specific age. But I was kinda hoping to work on something more practical which other people could use as well.

I had the idea of creating an application, sorta a graphical interface to Keras, which would allow the user to create the computation graph, make the training loop and normalize the data. After giving some thoughts to this idea, I came to the realization that such a tool can never offer the same range of possibilities a programmer would have with the library itself. Then I thought about targeting the practitioners and focusing more on the mainstream architectures but that sounded hard to finish in just one year, as well. I’m not sure if a beta version would be worth pursuing, with the hope that I could finish it during my masters.

The reason that I’m aiming at projects like this is that I’m hoping to get a scholarship to continue my studies abroad which I won’t be able to afford on my own in any way (I’m Iranian and if you check the value of our currency, you’d realize why). I’m hoping to prove my capabilities in my thesis project. My professor says that if I get even a single paper published, that’s enough for a bachelor student, but… you can never be too sure!

Another idea that I’m not exactly sure is worth the effort or not is working on some sort of a knowledge base for ML. With the every growing amount of publications in this field, it’s getting harder and harder to dive through them, specifically when it comes to comparing models. Now imagine a website with a taxonomy system that allows for a tree-like categorizing system which researchers could introduce their models in and others add their own experiences, implementations, etc. Unfortunately, I’d only be able to implement the website and absolutely no where near knowledgeable enough to populate it…

I’d greatly appreciate it if you could guide me towards a some type of a project that would be an impressive point in my resume to get an scholarship.

This is one of a series of small snippet-tutorials to share with you my experiences with APIs hacking and Tensorflow hacker.An effort to contribute to the community of Deep Learning software developers grow, from the basis of how to deal with Open Source Software (it obviously depends on language you are working, this time python).

https://uiuran.github.io/keras/tensorflow/deeplearning/2019/08/18/Keras-Deep-Learning-High-Level-API-Dismistified.html

Basically I am trying to find similarity of sentences in my native language. I created word embeddings using word2vec and fasttext. And used word mover distance, to find similar text. Now how do I determine the performance of each model. P.S there is no dataset for similar text in my native language.

Hi everyone,

https://pypi.org/project/transformer-model/

pip install transformer-model

I recently took some time to build out an extensible Transformer Model in TF2, mostly for my own future use cases but I thought I’d share with you and possibly get some feedback as well. I have not created many python packages, so if there’s something I missed or seems out of place feel free to create an issue on the repo.

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The goal of this project was to create all of the core pieces of the Transformer Model discussed in the “Attention is all you need” paper in a way that I could reuse them to create newer, more SOTA models like BERT and XLNet. I’ve left instructions on how to use this package to train a Transformer model and will be packaging this to go on pypi later today.

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My hope is this package saves someone some dev time. If it does, please give the package a star!