Hi r/machinelearning, I’m excited to announce the release of our new environment on robotic furniture assembly. We have over 80 furniture models from IKEA as well as support for Baxter and Sawyer robots (and a Cursor agent for those who don’t want to learn low-level control). We support a Gym interface and build on top of MuJoCo for ease of use with RL.

I’m excited to see what researchers will come up with to tackle furniture assembly, a complex and long-horizon task. Let me know if you have any questions or comments!

Website: www.clvrai.com/furniture Github: www.github.com/clvrai/furniture

Paper: https://arxiv.org/abs/1911.07246

I like machine learning. I’m interested in it. I don’t exactly understand how it works but I get the basic gist.

I was thinking… What if there was a website/social experiment thing where there was a machine learning bot and anyone could submit the training material? It would have a theme, maybe, and it would generate a result after a while.

Just an idea. I’m not smart enough to make it lol

(also posted to r/datascience but I realized this community is almost 10x bigger)

Hey all,

I’m a data scientist and looking to reddit for some advice… I’ve been in this role for about two years and have been the only data scientist that entire time – this was also my first data scientist role. Since entering into the new ‘data’ team about a year ago, we’ve been continuously plagued with issues like:

• strange and distracting projects by our boss (who has no analytics experience, but a long career of software developer management) – and a lack of him be able to understand scope and true effort required for these random projects
• lack of interest in hearing from Sr DAs and me on what our ideal working environments would be (warehouse design, what we can put on our VMs, etc)
• lack of him working with leadership to build an actual understanding of business needs
• exerting random/arbitrary control over how things get done (I’ve never seen him do this and it end up being a benefit to a project)

I’ve stayed in the role mainly because it was my foot in the door to this industry (which I am very grateful for), and at the beginning (and to this day, really) the amount of possibilities here are huge and exciting… if they could ever be executed properly as a team. And, to add difficulty to it even more, my boss is an overall great guy – I just don’t think he has the mental horsepower for such a huge change this late into career.

My main predicament is that I’ve been tasked with building out a customer engagement ‘engine’ of sorts – so attempting to predict individually customers likely to leave, and also understand customer cohorts that are more engaged. I’m approaching it similar to a customer churn model, but with a few differences. This has been hyped for a year- the board of directors is aware of it, and so is everyone in leadership. To say the least: the hype around the team he was supposed to build is huge. That pendulum is beginning to swing back in.

The problem is that because of my manager’s disjointed priorities, we have had no progress in building out a warehouse or pipeline that helps a data scientist/me in any way. So, I’m spending time crafting ETL around extremely messy and unreliable system data which has cost me a few months just to implement that – and it isn’t done, of course. He has made very little progress in figuring out that his mental model of what a data scientist does is mostly not true and that the slowness of this project is a result of the past year’s lack of a decent strategy.

And just for a quick, very cringe, example: a few weeks ago he was sweet-talked by a Harvard MBA type into trialing yet-another-vendor’s autoML solution, thinking that the reason why my project has taken months to get off of the ground was simply because I was having trouble building decent models (I haven’t been able to train a model, here, for months because the data didn’t exist in any usable way!). And this is *not* because I’ve been quiet about the real challenges – he simply does not listen to other points of view unless it’s coming down to him from his boss.

But – to be fair – I made several mistakes when joining his team- my #1 one being that I doubted my intuition around the team’s strategy. If someone has 10+ years experience managing software teams, he’s this confident, and I’m this new to the role, then I need to stop challenging the status quo he’s putting together.

Recently I learned that – finally – the screws are coming down on him from his boss and he’s been told to do several of the things I suggested months ago (petty for me to mention, but it feels good and is validating). But rather than him reaching out to me for advice on changing strategy, or what we can do to accelerate the project, it’s more of the same. It’s also too late at this point for me to give suggestions that our small team could do before the end of the year.

Skip to here if you don’t wanna read:

Previously, I’ve said to myself that I will stay at this company until I can put into production at least one model. The question I’m coming to is, what do I do when the timeline for that keeps extending indefinitely and you’ve lost faith in management to be where you need them to be? What do I do in my job hunt when they see I’ve been in a DS role for ~2 years and never got to implement a model into production? If I was a hiring manager, I’d assume to some extent that this person wasn’t in a real data scientist role and would doubt my skills/abilities. Of course I could just lie in an interview- but that feels extremely gross.

My solution so far is to do a few ambitious personal projects that flex on modeling, python ability, and creativity. But we all know that (at least traditionally), your professional experience is the most important factor.

So, if anyone has words of encouragement, discouragement, suggestions, whatever- I’d love to hear it. I doubt my situation is truly unique – and I also know things could be much worse. I am thankful to have gotten my foot in the door, which can be quite hard.

Thanks for reading

Knowledge distillation is a model compression technique whereby a small network (student) is taught by a larger trained neural network (teacher). The smaller network is trained to behave like the large neural network. This enables the deployment of such models on small devices such as mobile phones or other edge devices. In this guide, we’ll look at a couple of papers that attempt to tackle this challenge.

https://heartbeat.fritz.ai/research-guide-model-distillation-techniques-for-deep-learning-4a100801c0eb

A guide for astronauts (now, people doing machine learning) about what to do when things go wrong.

GitHub: https://github.com/bkkaggle/machine-learning-flight-rules

Product Hunt: https://www.producthunt.com/posts/machine-learning-flight-rules

There’s a lot of “hidden knowledge” online on places like Stackoverflow, Kaggle, and the Pytorch discussion forums that is really useful but not easily accessible to people who are just getting started with machine learning. This is why I made Machine learning flight rules, this Github repo compiles all of the things I have learned over the last two years about best practices, common mistakes, and little-known tricks when training neural networks. I’ve tried to make sure that all the information in this repository is accurate, but if you find something that you think is wrong, please let me know by opening an issue. This repository is still a work in progress, so if you find a bug, think there is something missing, or have any suggestions for new features, feel free to open an issue or a pull request. Feel free to use the library or code from it in your own projects, and if you feel that some code used in this project hasn’t been properly accredited, please open an issue. I named this project after the awesome Git Flight Rules project (https://github.com/k88hudson/git-flight-rules). I took a lot of tips from both Andrej Kaparthy’s blog post on a recipe for training neural networks (https://karpathy.github.io/2019/04/25/recipe/) and the Amid Fish blog post on lessons learned when reporoducing a deep reinforcement learning paper (http://amid.fish/reproducing-deep-rl)

[Discussion]

The data set is time series with 1 min frequency for the last four years. There are 40 features associated with the asset. Then there is a target which has a 0 when there is no fault and 1 when there is fault.

having this data, currently I have approached it in this way.

1. I have set the column 0 as index and set the type as date-time.
2. If the 39 features had any empty values in-between, I interpolated linearly for now to get the values assigned to them

Now when I browse online, I only see people picking one column and doing time series analysis to identify anomaly, in my case, I want to use the features in time sequence and then identify if the asset will fail or not in the future anytime/ any date.

Can someone help me understand what approach I need to take for this kind of a problem and also provide some sample for me to learn from.

I’ve typically only performed transfer learning via fine-tuning with neural networks (eg. image classifiers from pre-trained MobileNet, etc.), but does the same idea hold for a model like logistic regression or CRF? I’d argue yes because your essentially just training a new model with non-randomized initial weights (a prior). But am I missing something?

I’m currently looking into cross-domain transfer learning for non-neural NER models, and I wanted to fine-tune the weights of a pre-trained CRF with some newly annotated user-generated data.

A group of researchers who were working at NVIDIA has introduced Kaolin, a new PyTorch library with an aim to accelerate 3D deep learning research. Kaolin is home for future 3D DL research and you are welcome to make contributions.

Read more: https://medium.com/ai%C2%B3-theory-practice-business/pytorch-library-for-accelerating-3d-deep-learning-research-6b83df2073bf