Data Science by ODS.ai 🦜
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First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @haarrp
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New deep learning framework from Facebook

Pythia is a deep learning framework that supports multitasking in the vision and language domain. Built on our open-source #PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. #Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions.

Link: https://code.fb.com/ai-research/pythia/
GitHub: https://github.com/facebookresearch/pythia

#Facebook #FacebookAI #DL #CV #multimodal
​​Long-form question answering

Facebook AI shared the first large-scale data set, code, and baseline models for long-form QA, which requires machines to provide long, complex answers — something that existing algorithms have not been challenged to do before.

Link: https://ai.facebook.com/blog/longform-qa/

#FacebookAI #Facebook #NLP #NLU #QA
​​New fastMRI challenge from #FacebookAI team

Submission deadline: September 19

Announcement link: https://ai.facebook.com/blog/fastmri-challenge/
Competition link: https://fastmri.org/

#Competition #NotOnlyKaggle #Facebook #CV #DL
Self-supervised QA from Facebook AI

The researchers from Facebook AI published a paper with the results of exploring the idea of unsupervised extractive question answering and the following training of the supervised question answering model. This approach achieves 56.41F1 on SQuAD2 dataset.


Original paper: https://research.fb.com/wp-content/uploads/2019/07/Unsupervised-Question-Answering-by-Cloze-Translation.pdf?
Code for experiments: https://github.com/facebookresearch/UnsupervisedQA


#NLP #BERT #FacebookAI #SelfSupervised
Unsupervised Translation of Programming Languages

Model provided with Python, C++ or Java source code from GitHub, automatically learns to translate between the 3 languages in a fully unsupervised way.

Again: No supervision.

The correctness is then checked by compiling and running unit tests.

ArXiV: https://arxiv.org/pdf/2006.03511.pdf

#FAIR #FacebookAI #cs #unsupervised
​​A new SOTA on voice separation model that distinguishes multiple speakers simultaneously

Pandemic given a sufficient rise to new technologies covering voice communication. Noise cancelling is required more than ever and now #Facebook introduced a new method for separating as many as five voices speaking simultaneously into a single microphone. It pushes state of the art on multiple benchmarks, including ones with challenging noise and reverberations.

Blogpost: https://ai.facebook.com/blog/a-new-state-of-the-art-voice-separation-model-that-distinguishes-multiple-speakers-simultaneously
Paper: https://arxiv.org/pdf/2003.01531.pdf

#SOTA #FacebookAI #voicerecognition #soundlearning #DL
Deep learning to translate between programming languages

#FacebookAI released TransCoder, an entirely self-supervised neural transcompiler system that is claimed to make code migration easier and more efficient.

ArXiV: https://arxiv.org/pdf/2006.03511.pdf
Github: https://github.com/facebookresearch/TransCoder/

#NLU #codegeneration #NLP
​​Blender Bot 2.0: An open source chatbot that builds long-term memory and searches the internet

Bot is capable of supporting a dialog and remembering the context of the sequential questions.

Blogpost: https://ai.facebook.com/blog/blender-bot-2-an-open-source-chatbot-that-builds-long-term-memory-and-searches-the-internet
Github: https://github.com/facebookresearch/ParlAI
Paper 1: https://parl.ai/projects/sea
Paper 2: https://parl.ai/projects/msc

#chatbot #NLU #facebookai