Accelerating MRI reconstruction via active acquisition
Researchers from #Facebook AI propose a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.
Link: https://ai.facebook.com/blog/accelerating-mri-reconstruction/
Paper link: https://research.fb.com/publications/reducing-uncertainty-in-undersampled-mri-reconstruction-with-active-acquisition/
#CV #DL #CVPR2019 #healthcare #MRI #biolearning
Researchers from #Facebook AI propose a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.
Link: https://ai.facebook.com/blog/accelerating-mri-reconstruction/
Paper link: https://research.fb.com/publications/reducing-uncertainty-in-undersampled-mri-reconstruction-with-active-acquisition/
#CV #DL #CVPR2019 #healthcare #MRI #biolearning
Intro to Pythia — Visual Question Answering framework from Facebook
Pythia works in terms of #VQA by taking input picture and question and providing the answer to the latter in terms of picture semantics.
Link: https://link.medium.com/dknDKSuVqX
Previously: https://t.me/opendatascience/812
#DL #facebook #pythia #VQA #opensource
Pythia works in terms of #VQA by taking input picture and question and providing the answer to the latter in terms of picture semantics.
Link: https://link.medium.com/dknDKSuVqX
Previously: https://t.me/opendatascience/812
#DL #facebook #pythia #VQA #opensource
Medium
Pythia (Facebook) — Greek god doing Deep learning
“Artificial Intelligence” in 2019 has been exciting, Can it be more exciting than this? Guess what I found an answer for it and the answer…
💣New open-source recommender system from Facebook.
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
Link: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
Github: https://github.com/facebookresearch/dlrm
ArXiV: https://arxiv.org/abs/1906.03109
#Facebook #DLRM #recommender #DL #PyTorch #Caffe
Facebook open sourced video alignment algorithms that detect identical and near identical videos to build more robust defenses against harmful visual content.
Project page: https://newsroom.fb.com/news/2019/08/open-source-photo-video-matching/
Code: https://github.com/facebookresearch/videoalignment
#Facebook #video #cv #dl
Project page: https://newsroom.fb.com/news/2019/08/open-source-photo-video-matching/
Code: https://github.com/facebookresearch/videoalignment
#Facebook #video #cv #dl
Meta
Open-Sourcing Photo- and Video-Matching Technology to Make the Internet Safer | Meta
We're sharing some of the tech we use to fight abuse on our platform with others.
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
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
Open-sourcing hyperparameter autotuning for fastText
Facebook AI researchers are releasing a new feature for the fastText library which provides hyper-parameter autotuning for more efficient text classifiers.
Link: https://ai.facebook.com/blog/fasttext-blog-post-open-source-in-brief/
#FacebookAI #Facebook #FastText #NLU #NLP
Facebook AI researchers are releasing a new feature for the fastText library which provides hyper-parameter autotuning for more efficient text classifiers.
Link: https://ai.facebook.com/blog/fasttext-blog-post-open-source-in-brief/
#FacebookAI #Facebook #FastText #NLU #NLP
Meta
Open-sourcing hyperparameter autotuning for fastText
Facebook AI researchers are releasing a new feature for the fastText library that provides hyperparameter autotuning for more efficient text classifiers.
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
Submission deadline: September 19
Announcement link: https://ai.facebook.com/blog/fastmri-challenge/
Competition link: https://fastmri.org/
#Competition #NotOnlyKaggle #Facebook #CV #DL
Deep Fake Challenge by Facebook team
#Facebook launches a competition to fight deep fakes. Unfortunately, results of this competition will be obviously used to create better fakes, to the cheers of the people, wishing to watch the Matrix with Bruce Lee or more questionable deep fake applications.
Link: https://ai.facebook.com/blog/deepfake-detection-challenge/
#deepfake #video #cv #dl
#Facebook launches a competition to fight deep fakes. Unfortunately, results of this competition will be obviously used to create better fakes, to the cheers of the people, wishing to watch the Matrix with Bruce Lee or more questionable deep fake applications.
Link: https://ai.facebook.com/blog/deepfake-detection-challenge/
#deepfake #video #cv #dl
Video on how Facebook continues to develop its #Portal device
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
How #Facebook used Mask R-CNN, #PyTorch, and custom hardware integrations like foveated processing to improve Portal’s Smart Camera system.
Link: https://ai.facebook.com/blog/smart-camera-portal-advances/
#CV #DL #Segmentation
Meta
How we’ve advanced Smart Camera for new Portal video-calling devices
We’ve used Detectron2, Mask R-CNN, and custom hardware integrations like foveated processing in order to make additional speed and precision improvements in the computer vision models that power Smart Camera.
Online speech recognition with wav2letter@anywhere
Facebook have open-sourced wav2letter@anywhere, an inference framework for online speech recognition that delivers state-of-the-art performance.
Link: https://ai.facebook.com/blog/online-speech-recognition-with-wav2letteranywhere/
#wav2letter #audiolearning #soundlearning #sound #acoustic #audio #facebook
Facebook have open-sourced wav2letter@anywhere, an inference framework for online speech recognition that delivers state-of-the-art performance.
Link: https://ai.facebook.com/blog/online-speech-recognition-with-wav2letteranywhere/
#wav2letter #audiolearning #soundlearning #sound #acoustic #audio #facebook
HiPlot: High-dimensional interactive plots made easy
Interactive parameters' performance #visualization tool. This new Facebook AI's release enables researchers to more easily evaluate the influence of their hyperparameters, such as learning rate, regularizations, and architecture.
Link: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy
Github: https://github.com/facebookresearch/hiplot
Demo: https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html
Pip:
#hyperopt #facebook #opensource
Interactive parameters' performance #visualization tool. This new Facebook AI's release enables researchers to more easily evaluate the influence of their hyperparameters, such as learning rate, regularizations, and architecture.
Link: https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy
Github: https://github.com/facebookresearch/hiplot
Demo: https://facebookresearch.github.io/hiplot/_static/demo/demo_basic_usage.html
Pip:
pip install hiplot#hyperopt #facebook #opensource
Transferring Dense Pose to Proximal Animal Classes
Article on how to train DensePose for animals withiout labels
DensePose approach predicts the pose of humans densely and accurately given a large dataset of poses annotated in detail. It's super expensive to collect DensePose annotations for all different classes of animals. So authors show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in DensePose for humans. They propose to utilize the existing annotations of humans and do self-training on unlabeled images of animals.
Link: https://asanakoy.github.io/densepose-evolution/
YouTube: https://youtu.be/OU3Ayg_l4QM
Paper: https://arxiv.org/pdf/2003.00080.pdf
#Facebook #FAIR #CVPR #CVPR2020 #posetransfer #dl
Article on how to train DensePose for animals withiout labels
DensePose approach predicts the pose of humans densely and accurately given a large dataset of poses annotated in detail. It's super expensive to collect DensePose annotations for all different classes of animals. So authors show that, at least for proximal animal classes such as chimpanzees, it is possible to transfer the knowledge existing in DensePose for humans. They propose to utilize the existing annotations of humans and do self-training on unlabeled images of animals.
Link: https://asanakoy.github.io/densepose-evolution/
YouTube: https://youtu.be/OU3Ayg_l4QM
Paper: https://arxiv.org/pdf/2003.00080.pdf
#Facebook #FAIR #CVPR #CVPR2020 #posetransfer #dl
YouTube
DensePose applied on chimps: comparison of our method before self-training (left) and after (right)
Frame-by-frame predictions produced by our model before (teacher) and after self-training (student).
After self training the 24-class body part segmentation is more accurate and stable.
Project page: https://asanakoy.github.io/densepose-evolution/
After self training the 24-class body part segmentation is more accurate and stable.
Project page: https://asanakoy.github.io/densepose-evolution/
🤖 The NetHack Learning Environment
#Facebook launched new Reinforcement Learning environment for training agents based on #NetHack game. Nethack has nothing to do with what is considered common cybersecurity now, but it is an early terminal-based Minecraft (as a matter of fact one might say «console NetHack game» to go ‘all in’ in a word pun game).
NetHack is a wonderful RPG adventure game, happening in dungeon. Players control
#NLE uses python and ZeroMQ and we are looking forward to interesting applications or showcases to arise from this release.
Github: https://github.com/facebookresearch/nle
NetHack official page: http://nethack.org
#RL
#Facebook launched new Reinforcement Learning environment for training agents based on #NetHack game. Nethack has nothing to do with what is considered common cybersecurity now, but it is an early terminal-based Minecraft (as a matter of fact one might say «console NetHack game» to go ‘all in’ in a word pun game).
NetHack is a wonderful RPG adventure game, happening in dungeon. Players control
@ sign moving in ASCII-based environment, fighting enemies and doing quests. If you haven’t played it you are missing a whole piece of gaming culture and our editorial team kindly cheers you on at least trying to play it. Though now there lots of wikis and playing guides, canonicial way to play it is to dive into source code for looking up the keys and getting the whole idea of what to expect from different situations.#NLE uses python and ZeroMQ and we are looking forward to interesting applications or showcases to arise from this release.
Github: https://github.com/facebookresearch/nle
NetHack official page: http://nethack.org
#RL
Data Science by ODS.ai 🦜
🤖 The NetHack Learning Environment #Facebook launched new Reinforcement Learning environment for training agents based on #NetHack game. Nethack has nothing to do with what is considered common cybersecurity now, but it is an early terminal-based Minecraft…
Update from #Facebook on #Nethack learning Environment.
Link: https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning
Publication: https://arxiv.org/abs/2006.13760
#RL
Link: https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning
Publication: https://arxiv.org/abs/2006.13760
#RL
Making Animoji or any other 3D avatar more human-like
#Facebook researchers suggested an approach for more precise facial and gaze expression tracking.
Review: https://syncedreview.com/2020/07/07/facebook-introduces-integrated-eye-face-model-for-3d-immersion-in-remote-communication/
Paper: https://research.fb.com/wp-content/uploads/2020/06/The-Eyes-Have-It-An-Integrated-Eye-and-Face-Model-for-Photorealistic-Facial-Animation.pdf
#eyetracking #cv #dl #3davatar #videolearning #facerecognition
#Facebook researchers suggested an approach for more precise facial and gaze expression tracking.
Review: https://syncedreview.com/2020/07/07/facebook-introduces-integrated-eye-face-model-for-3d-immersion-in-remote-communication/
Paper: https://research.fb.com/wp-content/uploads/2020/06/The-Eyes-Have-It-An-Integrated-Eye-and-Face-Model-for-Photorealistic-Facial-Animation.pdf
#eyetracking #cv #dl #3davatar #videolearning #facerecognition
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
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
Data Science by ODS.ai 🦜
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…
#Facebook released github repo with code for #TransCoder : https://github.com/facebookresearch/TransCoder/
GitHub
GitHub - facebookresearch/TransCoder: Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf
Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf - facebookresearch/TransCoder
SEER: The start of a more powerful, flexible, and accessible era for computer vision
#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.
And here it is — model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.
Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)
Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf
#facebook #fair #cv #dl
#SEER stands for SElf-supERvised architecture which follows the vision of Yan LeCunn that real breakthrough in quality of models is possible only with #selfsupervised learning.
And here it is — model which was trained using some enormous amount of data achieves 84.2 percent top-1 accuracy on ImageNet.
Paramus: 1.3B
Dataset: 1B random images
Hardware: 512 GPUs (unspecified)
Blogpost: https://ai.facebook.com/blog/seer-the-start-of-a-more-powerful-flexible-and-accessible-era-for-computer-vision
ArXiV: https://arxiv.org/pdf/2103.01988.pdf
#facebook #fair #cv #dl
Meta
SEER: The start of a more powerful, flexible, and accessible era for computer vision
The future of AI is in creating systems that can learn directly from whatever information they’re given — whether it’s text, images, or another type of data — without relying on carefully curated and labeled data sets to teach them how to recognize objects…
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Habitat 2.0: Training home assistant robots with faster simulation and new benchmarks
Facebook released a new simulation platform to train robots in. Yeah, virtual robots in virtual environment, which can be a real space replica. This work brings us closer to domestic use of assistive robots.
Project website: https://ai.facebook.com/blog/habitat-20-training-home-assistant-robots-with-faster-simulation-and-new-benchmarks
Paper: https://ai.facebook.com/research/publications/habitat-2.0-training-home-assistants-to-rearrange-their-habitat
#Facebook #DigitalTwin #VR #RL #assistiverobots
Facebook released a new simulation platform to train robots in. Yeah, virtual robots in virtual environment, which can be a real space replica. This work brings us closer to domestic use of assistive robots.
Project website: https://ai.facebook.com/blog/habitat-20-training-home-assistant-robots-with-faster-simulation-and-new-benchmarks
Paper: https://ai.facebook.com/research/publications/habitat-2.0-training-home-assistants-to-rearrange-their-habitat
#Facebook #DigitalTwin #VR #RL #assistiverobots
🦜 Hi!
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
GitHub
ultimate_posts/where_to_start at master · open-data-science/ultimate_posts
Ultimate posts for opendatascience telegram channel - open-data-science/ultimate_posts