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[N] Awesome AI Research and Papers reviewed on Computer Vision News (with codes!) November 2019

[N] Awesome AI Research and Papers reviewed on Computer Vision News (with codes!) November 2019

The November issue of Computer Vision News: 38 pages about AI and Deep Learning through research and practical applications.

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Technical articles on pages 4-8 and 24-29. Subscribe for free on page 38.


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[Research] We trained a self-balancing physics-based character to follow interactive motion capture.

Here’s a twitter thread including a video of a ragdoll getting lots of cubes in the face:

Here’s a blog post:

Here’s a paper, which will be presented at Siggraph Asia next week:

And here’s a high level explanation of what this is all about:

Physics-based animation holds the promise of unlocking unprecedented levels of interaction, fidelity, and variety in games. The intricate interactions between a character and it’s environment can only be faithfully synthesized by respecting real physical principles. On the other hand, data-driven animation systems utilizing large amounts of motion capture data have already shown that artistic style and motion variety can be preserved even when tight constraints on responsiveness and motion control objectives are required by a game’s design.

To combine the strengths of both methods we developed DReCon, a character controller created using deep reinforcement learning. Essentially, simulated human characters learn to move around and balance from precisely controllable motion capture examples. Once trained, gamepad controlled characters can be fully simulated using physics and simultaneously directed with a high level of responsiveness at a surprisingly low runtime cost on today’s hardware.

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[R]Research Guide: Pruning Techniques for Neural Networks

Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It’s a model optimization technique that involves eliminating unnecessary values in the weight tensor. This results in compressed neural networks that run faster, reducing the computational cost involved in training the networks.

More at

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[D] Working on an ethically questionnable project…

Hello all,

I’m writing here to discuss a bit of a moral dilemma I’m having at work with a new project we got handed. Here it is in a nutshell :

Provide a tool that can gauge a person’s personality just from an image of their face. This can then be used by an HR office to help out with sorting job applicants.

So first off, there is no concrete proof that this is even possible. I mean, I have a hard time believing that our personality is characterized by our facial features. Lots of papers claim this to be possible, but they don’t give accuracies above 20%-25%. (And if you are detecting a person’s personality using the big 5, this is simply random.) This branch of pseudoscience was discredited in the Middle Ages for crying out loud.

Second, if somehow there is a correlation, and we do develop this tool, I don’t want to be anywhere near the training of this algorithm. What if we underrepresent some population class? What if our algorithm becomes racist/ sexist/ homophobic/ etc… The social implications of this kind of technology used in a recruiter’s toolbox are huge.

Now the reassuring news is that the team I work with all have the same concerns as I do. The project is still in its State-of-the-Art phase, and we are hoping that it won’t get past the Proof-of-Concept phase. Hell, my boss told me that it’s a good way to “empirically prove that this mumbo jumbo does not work.”

What do you all think?

submitted by /u/big_skapinsky
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[R] Teaching a neural network to use a calculator

Article by Reiichiro Nakano:

This article explores a seq2seq architecture for solving simple probability problems in Saxton et. al.’s Mathematics Dataset. A transformer is used to map questions to intermediate steps, while an external symbolic calculator evaluates intermediate expressions. This approach emulates how a student might solve math problems, by setting up intermediate equations, using a calculator to solve them, and using those results to construct further equations.

submitted by /u/wei_jok
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[D] BERT for non-textual sequence data

Hi there, I’m working on a deep learning solution for classifying sequence data that isn’t raw text but rather entities (which have already been extracted from the text). I am currently using word2vec-style embeddings to feed the entities to a CNN, but I was wondering if a Transformer (à la BERT) would be a better alternative & provide a better way of capturing the semantics of the entities involved. I can’t seem to find any articles (let alone libraries) to apply sth like BERT to non-textual sequence data. Does anybody know any papers about this angle? I’ve thought about training a BERT model from scratch and treating the entities as if they were text. The issue with that though is that apparently BERT is slow when dealing with long sequences (sentences). In my data I often have sequences that have a length of 1000+ so I’m worried BERT won’t cut it. Any help, insights or references are very much appreciated! Thanks

submitted by /u/daanvdn
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[D] Thoughts about this conversation?

This thread is on a public forum(Twitter) between two scientists.

Person1 – [Director of #AI #research @nvidia, Bren #Professor @Caltech, Fmr Principal scientist @awscloud]

Person2 – Research Scientist at Deepmind

Both are entitled to their own opinions. Here’s how the thread goes…

Person1(talking about her newly published work): DeepLearning is only good at interpolation. But applications need extrapolation that can reason about more complex scenarios than it is trained on. With current methods, accuracy degrades rapidly when complexity of test instances grows. Our new work aims to overcome this…

Person2: This tweet really downplays prior work. NTM, memory nets, Neural GPU, MANN, graph nets, and many, many other related methods also degrade gracefully. Your work looks like an important next step, but this rhetoric is unhelpful.

Person1: What you are doing is rhetoric and rude. We have mentioned all prior work in our paper. You don’t want to engage in science. It is inevitable to get attacked online as a woman. #deepmind can engage in all kind of media hype that is unethical but I get attacked for stating facts. As a woman stating science, I get accused of engaging in rhetoric.

I personally feel this response by Person1 to be extremely out of the blue. Putting aside the fact that Person1 is a Director @ NVIDIA + some title at Caltech and Person2 is a scientist as well @Google, let’s look at the simple conversation here. The thread started with a tweet about an interesting work. That was followed by a review directed only at the tweet being rhetoric. And it was then replied with something unimaginable. Am I the only one looking at this all confused?

Source post:

submitted by /u/GreySindrome
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“[D]” John Carmack stepping down as Oculus CTO to work on artificial general intelligence (AGI)

Here is John’s post with more details:

I’m curious what members here on MachineLearning think about this, especially that he’s going after AGI and starting from his home in a “Victorian Gentleman Scientist” style. John Carmack is one of the smartest people alive in my opinion, and even as CTO at Oculus he’s answered several of my questions via Twitter despite never meeting me nor knowing who I am. A real stand-up guy.

submitted by /u/jd_3d
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