I have just written an article about a benchmark I made of labeling tools for computer vision.

https://blog.sicara.com/best-open-source-annotation-tools-in-computer-vision-4b9f6a18f911

Do you know other tools which I should talk about?

Crosspost r/datascience

Suppose you’re working on a machine learning/coding contest and through your own research come up with a technique that is say, 4% better than the best thing anyone has tried (just pulling numbers out of the air here).

At what point is it better to not claim the prize on said contest and just keep the method secret? At what point should you publish it? Does it ever make sense to just use it in your own capacity analyzing data for companies as a 3rd party?

I mean I’m sure it’s all dependent on the money involved but one has to wonder where the breaking point is. What can you make as an independent 3rd party willing to do analysis with proprietary software you aren’t releasing?

In such a case, how could you ever provide confidence enough that the methods work? Also, how would you bill if essentially the time spent is mainly runtime? I’d love to hear any speculation, stories, industry standard behavior or history around this sort of thing.

A while ago I’ve seen a video of a talk by Ali Rahimi. It’s worth the 20 minutes for people who haven’t seen it!

I recently rewatched it and tried to look around if I could find papers which go into the direction of focusing more onto smaller ML experiments to build fundamental knowledge. But I’ve not found much aside from stuff on interpretability.

As a side note, I’m not an ML researcher but a physicist who uses ML in various aspects for work. I’d not consider myself an expert on any ML specific field but have an interest and try to keep up to date on things. From my personal experience, I’ve certainly felt (not sure how to describe it but let’s go with annoyed/demotivated) to use ML when it’s not clear to me why certain things work.

So a lot of times it seems more like Alchemy, in that I mix and match certain things until I get a result I like.

But why does it work? No clue.

Will it work for the next problem that is sufficiently different? No clue!

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I’d be very interested to hear what other people think or how they feel about this topic.

Also if you know of any paper seeking to build a more fundamental understanding, I’d appreciate a link 🙂

I was looking to extract if any news related to a company was positive or negative from news using python.

Which libraries would work good for such a project?

Silly question, but I haven’t been able to find any code examples on how to do this properly. I’m guessing you can’t add the label information to the first layer like you would in a dense VAE. Do you need to use a secondary layer that feeds the labels into the mean/std layer? Any advice would be appreciated!

In a new paper a team of researchers from Insilico Medicine present a new model called GENTRL (https://github.com/insilicomedicine/GENTRL) for molecule generation. This algorithm, given a protein target, has generated 6 viable compounds in 21 days, and after 25 more days of synthesis and testing, 4 passed the preliminary tests; the most potent one was tested on live mice, and its predicted biological and chemical properties were confirmed.

The model itself is quite interesting: they use a VAE with tensor trains as a latent space; this was pre-trained via regular ELBO maximization, and after that was optimised for novelty via REINFORCE. The code is freely available and is written in PyTorch.

Just like the last year, we’ve taught a summer school on Bayesian DL and are happy to share all the materials with anyone interested.

[ Videos | Slides | Practicals | Website ]

I’m trying to implement A2C in TF2.0 without using additional libraries like baselines. But I’m having problem with standard deviation(std) and clipping.

First of all, I made variable log_std to avoid 0s in calculations(I exponentiate it when I use log_std like tf.exp(log_std)). I’ve seen this trick in CS294-112 homework2, so I’m using it. But, when I gather trainable variables to train, I’m just using log_std, but is it okay to do this? (Since I made variable which is not exponentiated, so I think I can only update this, but not in exponentiated form) I feel like I shouldn’t since the values of derivative will be different when NN do back-propagating.

Second, I’m clipping actions with tf.clip_by_value(ac, env.action_space.low, env.action_space.high). But, I’m not sure how to clip NN’s output(NN is set to output mean of Gaussian distribution). NN should always output distributions with maximum : env.action_space.high and minimum : env.action_space.low as far as I know. But since I’m using Gaussian distribution, it is impossible to apply above constriction. Then what is usual way to clip NN’s output in case we use Gaussian distribution(or other distributions)?

Final question: do you think I should use RL libraries like tensorforce and baselines?