#Facebook released github repo with code for #TransCoder : https://github.com/facebookresearch/TransCoder/

Our friends from @loss_function_porn released their app and climbed AppStore top chart!

Let’s help them preserve that position by downloading an app and giving them 5⭐️.

​​Stanford updated tool Stanza with #NER for biomedical and clinical terms

Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.

That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.

Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640

#NLProc #NLU #Stanford #biolearning #medicallearning

​​Hope that someday DL industry will evolve enough to develop tools for recognizing russian doctors’ handwriting.

english to regex

generating regex by just describing it and providing an example (apparently powered by gpt-3)


web page: https://losslesshq.com

#regext #gpt3

​​Last day to apply for free Skoltech's Summer School of Machine Learning

Benefits of School:
+ top speakers from leading Data Science centers
+ new knowledge and advanced trends in statistical methods of machine learning.
+ free participation

How to apply:
Today is the LAST DAY to apply to school at the website

Link: https://smiles.skoltech.ru/school

#openedu #course #free #ml

Important information about the International Summer Online School of Machine Learning (SMILES):

We are often asked, what is a poster and why should you upload it if participation is free?

Let's go through this: submitting a poster about your project or research is a long-standing tradition at summer schools. The content should be informative, yet concise enough for the reader to understand your idea in 2 minutes or less.

What's the point?

Reason №1. The event will bring together top speakers, scientists, and entrepreneurs. So this is a good opportunity to get an expert opinion on your work, find partners for research, and potential investors and employers.

Reason №2. If you submit a poster, you will get access to the full range of events within SMILES: fireside chats, speed dating, social events, some lectures, etc.

Here are some examples of posters:
—​ https://bit.ly/2OSjfvs
—​ https://bit.ly/30H0XT7

If you still have questions, feel free to ask us in the comments. But If you don't, apply to SMILES and upload your poster right now:​ https://smiles.skoltech.ru/school

🚨Update: lectures will be available without registration 😍🤩🚨
🚨Update 2: poster examples🚨
— https://bit.ly/2OSjfvs
— https://bit.ly/30H0XT7

Image "Cloaking" for Personal Privacy

New research project from the University of Chicago CS group claims to provide a new face recognition protection mechanism.

Project link: https://sandlab.cs.uchicago.edu/fawkes/
Github: https://github.com/Shawn-Shan/fawkes

#Privacy #DL #CV #facerecognition

​​train your tf models on google cloud by tensorflow cloud

tf cloud is a python package that provides api for a transition from debugging and training keras & tf code in the local environment to distributed training in google cloud. it simplifies the process of training models on the cloud into a single, simple function call, requiring minimal setup and almost zero changes to model.
tf cloud handles cloud-specific tasks such as creating vm instances and distribution strategies for models automatically.


blog post: https://blog.tensorflow.org/2020/08/train-your-tensorflow-model-on-google.html?linkId=95907203
github: https://github.com/tensorflow/cloud

#tensorflow #cloud

generative elements of interior decoration by richard lord

​​Salesforce opensourced AI-framework for economic RL

AI Economist is capable of learning dynamic tax policies that optimize equality along with productivity in simulated economies, outperforming alternative tax systems.

Github: https://github.com/salesforce/ai-economist
Blog post with results: https://blog.einstein.ai/the-ai-economist/
Blog post with release: https://blog.einstein.ai/the-ai-economist-moonshot/

#Salesforce #gym #RL #economics #AIEconomics #animalcrossing #AIEconomist

​​announcing scann: efficient vector similarity search
ruiqi guo, philip sun, erik lindgren, quan geng, david simcha, felix chern, & sanjiv kumar @ google research

scann is a method for efficient vector similarity search at scale. them implements includes search space pruning & quantization for maximum inner product search & also supports other distance functions such as euclidean distance
the implementation is designed for x86 processors with avx2 support
scann achieves sota performance on ann-benchmarks.com as shown on the glove-100-angular dataset on the attached


blog post: https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html
paper: https://arxiv.org/abs/1908.10396
github: https://github.com/google-research/google-research/tree/master/scann

#icml2020 #similarity #scann #annoy

Gentle reminder that comments are available for some posts.

Click button 'Comments' and ask questions or share your opinion.

in the walls
by matt bierner

to make a scene from a horror movie when a face comes out of a wall like the 1st season of "Very Strange Things"
based on the arkit with ar & facetracking from the front camera


on the app store only: https://apps.apple.com/ru/app/in-the-walls/id1522257130?l=en

#arkit #ar #app

Caption: ArXiV paper, in Experiment, in Production

Source: https://vk.com/wall-166490678_545

Simple scalable graph neural networks

Michael Bronstein continues a marathon of great blog posts on GML. In a new post he describes their recent work on scaling GNNs to large network. There is a good introduction to sampling-based methods (e.g. SAGE, GraphSAINT, ClusterGCN), which sample a subgraph of a large graph and then train GNN only on a subgraph.

Then, he describes that it can be beneficial just precompute r-hop matrices, A^r X, and use MLP on these features. This way, you use topology of your graph and you apply mini-batch training with MLP.

What's cool is that the algorithm is already available in pytorch-geometric as a transform, which is implemented via sparseTensor matrix multiplication.

📝 Post "Simple scalable graph neural networks" published, discuss!