We’ve all come to terms with a neural network doing jobs such as handwriting recognition. This panel of special glass requires no electrical power, and is able to recognize gray-scale handwritten numbers. The glass contains precisely controlled inclusions such as air holes or an impurity such as graphene or other material. The team thinks they could do better if they allowed looser constraints on the glass manufacturing.

https://hackaday.com/2019/07/16/neural-network-in-glass-requires-no-power-recognizes-numbers/

At the start of the month Large Scale Adversarial Representation Learning was released, and it quickly became the most “hyped” article on Arxiv-sanity. I wrote this post to explain the contents of the paper and interpret the results.

https://stephanheijl.com/notes_on_large_scale_adversarial_learning.html

Hi,

I’m studying mechanical engineering and always have been interested in machine learning. I got through some basic tutorials online, the Google Crashcourse and currently am digging through the Hands-on Machine Learning book from Aurelien Geron as a side project of mine.

Right now I kind of want to apply my learned knowledge in my bachelor thesis. I will write it in the chair of Optimization of mechanical structures at my university and my professor proposed, that I pick Topology Optimization using deep learning as my topic.

Now my question: Is it possible, that I succeed in doing this project? I’m kind of intimidated, mainly because I never made my own project and/or did scientific research to such extend, but I like a challenge if it’s a realistic one. I have around 4 1/2 -5 Months.

If it’s possible :

What material should I get into? It seems, that a CNN will be the best bet for that project, any advice for that field?

If not:

Could you think of another topic I could pick, combining my knowledge of Machine Learning with Optimization of Mechanical Structures?

Thanks in advance

Hello /r/MachineLearning,

I am currently applying to some ML teams at Facebook, and was wondering how much mobility there is within different teams once you are in?

I applied for a few positions and am currently being interviewed for a role either with AML or with a product team. Is it possible to migrate between these two once I am employed there? And is there any path to getting into FAIR from one of the more applied teams, with a year or two of experience, through some internal transfer?

I don’t know any one working within Facebook who’d have some first hand experience so it would be very useful to have some information before I sign an offer and find myself stuck in a position/team that I don’t really like. The salary and conditions are great but I would like to be more on the research side, and am a bit worried of getting pigeon-holed as an applied product guy if I join.

Finally, do you think it would be relatively easy to get a job in a more research-oriented team in another company after some time within an applied ML facebook team?

Thanks for any advice you might have!

https://www.fakename.xyz/

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Silly little website which makes random names from char-RNN generated first and last names.

Name lists can be found on GitHub under a CC license:

https://github.com/artBoffin/rnn-names

Hi all,

My team is working on nontrivial multiclass text classification problems involving noisy datasets and usually more than 20 different target labels. We needed a robust way to combine both text features (ideally, leveraging some kind of pretrained word vectors) and extra-linguistic metadata.

So we figured out a Concat Model based on fast.ai that combines both ULMFiT for text and categorical or continuous input features to perform the task of classification. This article contains more details about the problem we’re solving and the results. Wanna take a look at the code? Check out this public Kaggle kernel and let us know what you think 🙂 Do you know other ways of exploiting both types of features?

Hi there, we’re a London-based research team working on clinical applications of machine learning. Recently, we’ve been dealing a lot with clinical datasets that exceed 1M+ observations and 20K+ features. We found that traditional dimensionality reduction and feature extraction methods don’t deal well with this data without subsampling and are actually quite poor at preserving both global and local structures of the data. To address these issues, we’ve been looking into Siamese Networks for non-linear dimensionality reduction and metric learning applications. We are making our work available through an open-source project: https://github.com/beringresearch/ivis

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So far, we’ve applied ivis to single cell datasets, images, and free text – we’re really keen to see what other applications could be enabled! We’ve also ran a large number of benchmarks looking at both accuracy of embeddings and processing speed – https://bering-ivis.readthedocs.io/en/latest/timings_benchmarks.html – and can see that ivis begins to stand out in datasets with 250K+ observations. We’re really excited to make this project open source – there’s so much for Siamese Networks beyond one-shot learning!