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: @malev
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​​Plato Research Dialogue System: A Flexible Conversational AI Platform

The Plato Research Dialogue System is a platform #Uber developed to enable experts and non-experts alike to quickly build, train, and deploy conversational AI agents.

Link: https://eng.uber.com/plato-research-dialogue-system/

#ConversationalAI #converstaion #NLP #NLU
​​Model for tweaking graph visualization layout parameters

New #MachineLearning model builds a WYSIWYG interface to intuitively produce a layout you want!

Demo: http://kwonoh.net/dgl
Paper: http://arxiv.org/abs/1904.12225

#Visualization #ML
​​GSCNN: video segmetation architecture

Semantic segmentation GSCNN significantly outperforms DeepLabV3+ on Cityscapes benchmark.

Paper: https://arxiv.org/abs/1907.05740
Github (Project): https://github.com/nv-tlabs/GSCNN

#DL #CV #NVidiaAI #Nvidia #autonomous #selfdriving #car #RL #segmentation
​​Large Scale Adversarial Representation Learning

DeepMind shows that GANs can be harnessed for unsupervised representation learning, with state-of-the-art results on ImageNet. Reconstructions, as shown in paper, tend to emphasise high-level semantics over pixel-level details.

Link: https://arxiv.org/abs/1907.02544

#DeepMind #GAN #CV #DL #SOTA
Top 8 trends from ICLR 2019

Overview of trends on #ICLR2019:
1. Inclusivity
2. Unsupervised representation learning & transfer learning
3. Retro ML
4. RNN is losing its luster with researchers
5. GANs are still going on strong
6. The lack of biologically inspired deep learning
7. Reinforcement learning is still the most popular topic by submissions
8. Most accepted papers will be quickly forgotten

Link: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html

#ICLR #overview
​​The new ResNet PoseNet model is much more accurate than the MobileNet one (the trade off being size & speed). The model is quantized & 25MB.
Pose estimation model, capable of running on devices

This model is really great for art installations or running on desktops.

Demo (requires camera, will work on desktop): https://storage.googleapis.com/tfjs-models/demos/posenet/camera.html?linkId=69346544
Github: https://github.com/tensorflow/tfjs-models/tree/master/posenet

#tensorflow #tensorflowjs #js #pose #poseestimation #posenet #ResNet #device #ondevice
Estimating the success of re-identifications in incomplete datasets using generative models

99.98% of Americans would be correctly re-identified in any dataset using 15 demographic attributes, suggesting that even heavily sampled anonymized datasets are unlikely to satisfy the modern standards for anonymization set forth by GDPR.

This is a big concern about privacy and a problem for Data Engineering, especially for those working with anonymized personal information. Paper provides a way to re-identify person from anonymized dataset, this can be useful for people who work for government or security companies

https://www.reddit.com/r/science/comments/chko43/9998_of_americans_would_be_correctly_reidentified/

#privacy #gdpr #federatedlearning #ml
​​Baidu’s Optimized ERNIE Achieves State-of-the-Art Results in Natural Language Processing Tasks

#Baide developed ERNIE 2.0, a continual pre-training framework for language understanding. The model built on this framework has outperformed #BERT and #XLNet on 16 tasks in Chinese and English.

Link: http://research.baidu.com/Blog/index-view?id=121

#NLP #NLU
​​πŸ”₯Interactive demo of GAN turning doodles into beautiful pictures

NVidia released #GauGAN for anyone to use. Trained on 1M images, the #GAN tool automatically turns doodles into photorealistic landscapes.

Project page: https://www.nvidia.com/en-us/research/ai-playground/
Interactive demo: http://nvidia-research-mingyuliu.com/gaugan

#Nvidia #CV #DL
21k followers β€” best feedback from the audience!

Thank you!
ODS breakfast in Paris! See you this Saturday at 10:30 at Malongo CafΓ©, 50 Rue Saint-AndrΓ© des Arts.
​​New paper on training with pseudo-labels for semantic segmentation

Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.

Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445

#GCPR2019 #Segmentation #CV
​​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 β€” something that existing algorithms have not been challenged to do before.

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

#FacebookAI #Facebook #NLP #NLU #QA
ODS breakfast in Paris! See you this Saturday at 10:30 at Malongo CafΓ©, 50 Rue Saint-AndrΓ© des Arts.