dm2gym: Convert DeepMind Control Suite to OpenAI gym environments.

Here’s the link

https://tensorflow.devpost.com/

We are looking for one more member, ideally someone with experience some of the current SoTA NLP models (Elmo, Transformer, BERT, GPT/2, ULMFiT, etc.) and wrangling data for those datasets (Our adviser may have had their name published in the official paper for one of those papers 😉 ) . But really, we’re just looking for someone who has solid practical experience with Tensorflow and can data wrangle.

If you’re interested, PM me with what are your time commitments for the next 3 weeks, and your experience with Tensorflow.

Hi

I’m looking into computational argumentation, e.g. the IBM Project Debater is a great example. I couldn’t find much on using large knowledge bases, e.g. Wikidata, Google Knowledge Graph API and similar to use the information to formulate statements.

Do you know about interesting projects in the field? Also, IBM hasn’t published much on their system (they did publish a few papers on subtopics tho), so if you know anything similar that could we amazing.

Thanks

Im working on a project that requires running a webcam feed through a network in near real time. I have a network that works, and I have a media server set up with a plugin that process the images, but I am not sure what the best way to actually use the model is in the media server.

Should I plug the network directly into the plugin? Should the network live elsewhere and the plugin send data to it through some RPC or REST calls?

Thank you!

Human Pose estimation is an important problem that has enjoyed the attention of the Computer Vision community for the past few decades and is a crucial step towards understanding people in images and videos. This post covers the basics of Human Pose Estimation (2D) and reviews the literature on this topic.

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Article Link : https://blog.nanonets.com/human-pose-estimation-2d-guide

I’m very new to Knowledge Graphs and I would like to learn a bit about them. I’ve done some research and from what I see, the first step would be to generate triplets. Then, the actual graph needs to be trained. Finally, there’s the issue of querying and visualizing the graph. Is this interpretation correct?

Are there any python packages that can help with building my own KG? I have not been too successful in finding clear resources, so anything would be helpful. If the approach is built around spaCy, even better!

Use case: I have a decent dataset of news articles and embeddings. Would like to take advantage of the dataset and learn a new skill in the meantime.

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Thanks!

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Edit with resources I’ve found:
Nice (theoretical) slides: https://kgtutorial.github.io/

Specifically, it’ll be great to hear on the differences as they apply to doing research work vs applied work along the lines of ease of use and performance of models?

Fun categorical classification project, I’d call it ass-o-meter. I queried 1M 2019_01 comments from gbq and running my model on Google colab.

I’m starting to do research in an area of ML that is new to me. After a few weeks of digging and reading, I’ve identified around 20-30 important papers in this area. I’m trying to find the most efficient way (in terms of speed vs knowledge retained) to read, understand, and retain these papers.

Does anyone here have any strategies/templates they use in these circumstances?

So far, I’m thinking:

– 1 markdown/latex file per paper with abstract + my own bullet point notes

– A mindmap or some similar visualization that connects all the papers

– Storing this along with the pdfs of the papers as a git repo to make navigation easy

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Looking for both reading/understanding strategies as well as summarization/organization ones.