ββDeep Learning Image Segmentation for Ecommerce Catalogue Visual Search
Microsoftβs article on image segmentation
Link: https://www.microsoft.com/developerblog/2018/04/18/deep-learning-image-segmentation-for-ecommerce-catalogue-visual-search/
#CV #DL #Segmentation #Microsoft
Microsoftβs article on image segmentation
Link: https://www.microsoft.com/developerblog/2018/04/18/deep-learning-image-segmentation-for-ecommerce-catalogue-visual-search/
#CV #DL #Segmentation #Microsoft
ββGoogle AI research on learning better simulation methods for partial differential equations
New research shows how machine learning can improve high-performance computing for solving partial differential equations, with potential applications that range from modeling #climatechange to simulating fusion reactions. Learn all about it here
Link: https://ai.googleblog.com/2019/07/learning-better-simulation-methods-for.html
#PDE #DE #GoogleAI
New research shows how machine learning can improve high-performance computing for solving partial differential equations, with potential applications that range from modeling #climatechange to simulating fusion reactions. Learn all about it here
Link: https://ai.googleblog.com/2019/07/learning-better-simulation-methods-for.html
#PDE #DE #GoogleAI
On the concept of 'intellectual debt'
There is technical debt β when you know you should rewrite some stuff, or implement some features, but they don't seem critical at the moment. So article introduces a concept of 'intellectual debt', which resies with more broad and common use of #MachineLearning and #DeepLearning (specially, the latter). What happens when AI gives us seemingly correct answers that we wouldn't have thought of ourselves, without any theory to explain them?
Link: https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking
#Meta #common #lyrics
There is technical debt β when you know you should rewrite some stuff, or implement some features, but they don't seem critical at the moment. So article introduces a concept of 'intellectual debt', which resies with more broad and common use of #MachineLearning and #DeepLearning (specially, the latter). What happens when AI gives us seemingly correct answers that we wouldn't have thought of ourselves, without any theory to explain them?
Link: https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking
#Meta #common #lyrics
The New Yorker
The Hidden Costs of Automated Thinking
Overreliance on artificial intelligence may put us in intellectual debt.
ββNew dataset with adversarial examples
Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months.
ArXiV: https://arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#Dataset #Adversarial
Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months.
ArXiV: https://arxiv.org/abs/1907.07174
Dataset and code: https://github.com/hendrycks/natural-adv-examples
#Dataset #Adversarial
ββRelease of 27 pretrained models for NLP / NLU for PyTorch
Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.
Link: https://medium.com/dair-ai/pytorch-transformers-for-state-of-the-art-nlp-3348911ffa5b
#SOTA #NLP #NLU #PyTorch #opensource
Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.
Link: https://medium.com/dair-ai/pytorch-transformers-for-state-of-the-art-nlp-3348911ffa5b
#SOTA #NLP #NLU #PyTorch #opensource
ODS breakfast in Paris! See you this Saturday at 10:30 at Malongo CafΓ©, 50 Rue Saint-AndrΓ© des Arts.
Filter autoselect in VSCO by Google
#VSCO used #TensorFlow Lite to develop the 'For This Photo' feature, which uses on-device ML to suggest photo filter presets from a curated list.
YouTube: https://www.youtube.com/watch?v=fHbjfeitIvE
Link: https://medium.com/tensorflow/suggesting-presets-for-images-building-for-this-photo-at-vsco-9b94041c4ba4
#mobile #device #cv #dl
#VSCO used #TensorFlow Lite to develop the 'For This Photo' feature, which uses on-device ML to suggest photo filter presets from a curated list.
YouTube: https://www.youtube.com/watch?v=fHbjfeitIvE
Link: https://medium.com/tensorflow/suggesting-presets-for-images-building-for-this-photo-at-vsco-9b94041c4ba4
#mobile #device #cv #dl
YouTube
VSCO β For This Photo
Baidu's recent paper: Hubless Nearest Neighbor Search
Hubless Nearest Neighbor Search, a new method for Bilingual Lexicon Induction, improves retrieval accuracy significantly. Empirical results show HNN outperforms NN, ISF and other state-of-the-art.
Github: https://github.com/baidu-research/HNN
Paper: https://github.com/baidu-research/HNN/blob/master/doc/HNN.pdf
#ACL2019 #NLP #NLU
Hubless Nearest Neighbor Search, a new method for Bilingual Lexicon Induction, improves retrieval accuracy significantly. Empirical results show HNN outperforms NN, ISF and other state-of-the-art.
Github: https://github.com/baidu-research/HNN
Paper: https://github.com/baidu-research/HNN/blob/master/doc/HNN.pdf
#ACL2019 #NLP #NLU
GitHub
baidu-research/HNN
Contribute to baidu-research/HNN development by creating an account on GitHub.
ββPlato 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
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
ββModel 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
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
ββGSCNN: 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
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
ββLarge 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
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
Overview of 10 Stanford's Data Science courses
A survivorβs guide to Artificial Intelligence courses at Stanford
Link: https://huyenchip.com/2018/03/30/guide-to-Artificial-Intelligence-Stanford.html
#Staford #MOOC #course #free #rating #learning
A survivorβs guide to Artificial Intelligence courses at Stanford
Link: https://huyenchip.com/2018/03/30/guide-to-Artificial-Intelligence-Stanford.html
#Staford #MOOC #course #free #rating #learning
Chip Huyen
A survivorβs guide to Artificial Intelligence courses at Stanford (Updated Feb 2020)
Twitter thread
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
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
Huyenchip
Top 8 trends from ICLR 2019
[Twitter thread] Disclaimer: This post doesnβt reflect the view of any of the organizations Iβm associated with and is probably peppered with my personal and...
ββThe 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
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
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
reddit
99.98% of Americans would be correctly re-identified in any...
Posted in r/science by u/FvDijk β’ 350 points and 30 comments
π₯Neural Point-Based Graphics
Breakthrough work on generating realistic 3D scenes in AR.
Demo: https://www.youtube.com/watch?v=7s3BYGok7wU
Project page: https://dmitryulyanov.github.io/neural_point_based_graphics
ArXiV: https://arxiv.org/pdf/1906.08240.pdf
#CV #AR #DL #SamsungAI
Breakthrough work on generating realistic 3D scenes in AR.
Demo: https://www.youtube.com/watch?v=7s3BYGok7wU
Project page: https://dmitryulyanov.github.io/neural_point_based_graphics
ArXiV: https://arxiv.org/pdf/1906.08240.pdf
#CV #AR #DL #SamsungAI
YouTube
Neural Point-Based Graphics
Full paper https://arxiv.org/abs/1906.08240
Project page https://dmitryulyanov.github.io/neural_point_based_graphics
Discussion on Reddit: https://www.reddit.com/r/MachineLearning/comments/chc220/research_neural_pointbased_graphics
#computergraphics #pointcloudβ¦
Project page https://dmitryulyanov.github.io/neural_point_based_graphics
Discussion on Reddit: https://www.reddit.com/r/MachineLearning/comments/chc220/research_neural_pointbased_graphics
#computergraphics #pointcloudβ¦
ββBaiduβs 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
#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
ββπ₯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
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
Great article on text preprocessing, covering cleaning, #tokenization, #lemmatization and other aspects
Link: https://medium.com/@datamonsters/text-preprocessing-in-python-steps-tools-and-examples-bf025f872908
#NLP #NLU #datacleaning
Link: https://medium.com/@datamonsters/text-preprocessing-in-python-steps-tools-and-examples-bf025f872908
#NLP #NLU #datacleaning
Medium
Text Preprocessing in Python: Steps, Tools, and Examples
by Olga Davydova, Data Monsters
21k followers β best feedback from the audience!
Thank you!
Thank you!