I was thinking about why the training process for image recognition is such a resource intensive task, and I got caught up on the idea that information loss caused by using projections (i.e. pictures) as training data would be an interesting area to explore.

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In comes mesh based 3d models. The training subject now has all of the necessary features in a single example My question is twofold. Do you see any potential in using 3d models as a source for an ML algo/NN, and if yes, how would you go about doing it?

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I’ve done a bit of brain storming, but I can’t really get anywhere. My initial thoughts were decompose the problem into parts, and solve it from both ends. One challenge is getting an isolated object from an image to use as a target, and the other is decomposing a 3d model into a useful 2d projection that can be compared with the target.

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Figuring that the 2D part would be easier, I decided to tackle that first, and I found the task of meaningful image segmentation to be a bit more difficult than I expected. It essentially came down to this: I can use pre existing methods to segment an image, or I can make my own; but nothing actually does decent job of pulling out objects with the exception of NNs which need to be trained to do so. In an attempt to improve the training process, I essentially run into the need to use methods that I’m trying to replace.

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The 3D part has a variety of challenges as well. I figured I could do something like take an image of the model that’s in similar proportion to the image that contains my target, then try to align the centroid of the model with the centroid of the target. This would eventually get to needing to iteratively translate, rotate, and scale the model. Some other obvious issues include segmenting the 3d model, posing the model, and finding what model to use as a reference when there are multiple ones saved.

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Thoughts?

We have just released a new version of AmpliGraph. It is available from pypi, all you have to do is run pip install –upgrade ampligraph. It requires TensorFlow 1.13 or 1.14 to be installed.

For beginners, we have two new tutorials: * Basics with Game of Thrones data * Clustering and classification with football data

For advanced users, we have a new knowledge discovery API, including discover facts, clustering, near-duplicates detection, and topn query.

We also provide state-of-the-art results on two benchmark datasets (WN18RR and YAGO3-10), plus competitive results on other benchmarks. These results are fully reproducible.

The updated documentation with the complete changelog is available here: http://docs.ampligraph.org

If you have any questions or feedback, you can comment here or raise an issue on GitHub.

Hi all,

I was checking out this video about an ‘AI-enabled speech pronunciation coaching app’ where it records your voice and gives feedback on your (English) pronunciation. With details on which part of the word needs more practice. Please watch a snippet of the video demonstrating it here.

Can someone recommend me papers/repositories explaining the algorithms and method that (most likely) enables the app to do exactly this?

My take on it will be using ASR (Automatic Speech Recognition) to do speech to phonemes and phonemes to a special grammar (e.g. ARPABET). Then the recognised speech, in the special grammar, is compared to the truth. If it matches, then your pronunciation is perfect. Otherwise, it will give you feedback on the specific part of the word that needs practice.

Thanks!

I’ve been seeing the Intel NUC 8 mini PCs going on sale. Specifically the NUC8i7HNK2 with an i7-8705G 3.10 Ghz, Kaby Lake G processor and a Radeon RX Vega M GL is on sale for $700 (pretax) without the RAM or SSD.

This is a great value! I need a portable machine learning teaching tool. A big bulky gaming GPU laptop is not that great (unwieldy, price and performance wise). These Intel NUCs you can just throw in the luggage and plug it in anywhere to do a demo or try out an idea before I go out and rent a cloud GPU to do the heavy lifting.

But is that RX Vega M GPU any good for machine learning?

I know last year they didn’t support CUDA but is this still an issue with libraries? I tried digging into documentation but it’s unclear.

Disclaimer – this is not my project, but it is simple and very cool at the same time, so I thought people might like it.

Say hello to Hunga Bunga!

Like the title says, it lets you brute force all of sklearn (including parameters and model types) with a simple fit / predict interface.

All you need to do is:

from hunga_bunga import HungaBungaClassifier, HungaBungaRegressor clf = HungaBungaClassifier() clf.fit(x, y) clf.predict(x) 

As simple as that.

Do you have any other simple yet wonderful automation libraries? I’d love to know.

Linking data or model files makes it possible to rearrange any amount of files very fast, while avoiding copying, thus saving disk space. Data science teams use symlinks to save space and avoid copying large datasets.

The tutorial start with a strategy of copying files into place, then using hard links and symbolic links, then ending up with a new type of link, reflinks, which implements Copy On Write capabilities available in modern file systems on Linux and Mac OS: Reflinks vs symlinks vs hard links, and how they can help machine learning projects

For a single learning approach, you can do F1 score measures, but how will you evaluate a probability distribution? (i.e. compare [0.1,0.2,0.5,0.2] and [0.2,0.1,0.5,0.2]) My reading on the area has pointed to KL-divergence to measure the information loss, but any other methods to consider?

concerning https://arxiv.org/abs/1806.04418

I understand that the forward pass needs to be adapted for QRNN; but what’s unclear to me:

Why do they derive a backprop algorithm especially for quaternions? Shouldn’t the automatic differentiation frameworks like Pytorch or Tensorflow do that automatically, at least in the case when the activation function is as they used it? In the final end, I guess everything is implemented by “real”-valued matrices and if the forward pass is according to quaternionic multiplication, why would an automatically derived gradient not work? So my question would be is there any difference of their backward pass to pytorch derived backward pass, apart from computational speed or memory consumption? If I understand correctly, in the copy-task of the github repo (https://github.com/Orkis-Research/Quaternion-Recurrent-Neural-Networks) even a pytorch backward pass is used by default?

Thanks for any answer or intuition that either confirms what I think or contradicts me.

I want to host some of my models as APIs for a side project, wondering if anyone has done this on typical web hosts/ how well would that go? Looking to host some BERT models for example, which take a few seconds even on my GPU inference so is it doable on web host CPUs?

TalkToTransformer website seems to be hosting GPT2 models and can handle queries quite well, anyone know how that is running?

Obviously would like to keep costs low otherwise would just rent a server with GPU..