Data Science by ODS.ai π¦
ββA Style-Based Generator Architecture for Generative Adversarial Networks Code and pre-trained models for Style-GAN paper. Github: https://github.com/NVlabs/stylegan ArXiV: http://stylegan.xyz/paper #GAN #CV #DL
Application of #StyleGAN: site, which generates new people every page refresh.
Link: https://thispersondoesnotexist.com
Link: https://thispersondoesnotexist.com
OpenAIβs new model can generate surprisingly realistic fake news.
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization β all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization β all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
Data Science by ODS.ai π¦
Neural networks #architecture #cheatsheet #nn
One of the most complete lists of #NN types.
Link: http://www.asimovinstitute.org/neural-network-zoo/
Link: http://www.asimovinstitute.org/neural-network-zoo/
The Asimov Institute
The Neural Network Zoo - The Asimov Institute
With new neural network architectures popping up every now and then, itβs hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheatβ¦
ββObject Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis.
#DL automated microscopy with objection detection.
Paper: https://www.biorxiv.org/content/10.1101/544833v1
#AugmentedReality
#DL automated microscopy with objection detection.
Paper: https://www.biorxiv.org/content/10.1101/544833v1
#AugmentedReality
ββSC-FEGAN: Face Editing Generative Adversarial Network with Userβs Sketch and Color
New paper on architecture that lets you add/change: earrings, glasses, hair style, dimples, & more. Sketches are trasformed to the photo by #GAN network.
ArXiV: https://arxiv.org/pdf/1902.06838.pdf
Code: https://github.com/JoYoungjoo/SC-FEGAN
New paper on architecture that lets you add/change: earrings, glasses, hair style, dimples, & more. Sketches are trasformed to the photo by #GAN network.
ArXiV: https://arxiv.org/pdf/1902.06838.pdf
Code: https://github.com/JoYoungjoo/SC-FEGAN
"Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet"
A "bag of words" of nets on tiny 17x17 patches suffice to reach AlexNet-level performance on ImageNet. A lot of the information is very local.
Paper: https://openreview.net/forum?id=SkfMWhAqYQ
#fun #CNN #CV #ImageNet
A "bag of words" of nets on tiny 17x17 patches suffice to reach AlexNet-level performance on ImageNet. A lot of the information is very local.
Paper: https://openreview.net/forum?id=SkfMWhAqYQ
#fun #CNN #CV #ImageNet
OpenReview
Approximating CNNs with Bag-of-local-Features models works...
Aggregating class evidence from many small image patches suffices to solve ImageNet, yields more interpretable models and can explain aspects of the decision-making of popular DNNs.
Probabilistic foundations of econometrica: part 1
Great intro into #statistics basics.
Link: https://freakonometrics.hypotheses.org/57649
#beginner #novice #entrylevel
Great intro into #statistics basics.
Link: https://freakonometrics.hypotheses.org/57649
#beginner #novice #entrylevel
Freakonometrics
Probabilistic Foundations of Econometrics, part 1
In a series of posts, I wanted to get into details of the history and foundations of econometric and machine learning models. It will be some sort of online version of our joint paper with Emmanuel Flachaire and Antoine Ly, Econometrics and Machine Learningβ¦
Analyzing Experiment Outcomes: Beyond Average Treatment Effects
Good #statistics article on why tail distribution and #experimentdesign matters. Quantile treatment effects (QTEs) helps to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the #Uber marketplace.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
Good #statistics article on why tail distribution and #experimentdesign matters. Quantile treatment effects (QTEs) helps to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the #Uber marketplace.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
ββPseudo-extended Markov chain Monte Carlo
Pseudo-Extended #MC for easier sampling from multimodal posteriors. Extend the target distribution and then run your favourite sampler (f.e. #HMC).
ArXiV: https://arxiv.org/abs/1708.05239
#statistics
Pseudo-Extended #MC for easier sampling from multimodal posteriors. Extend the target distribution and then run your favourite sampler (f.e. #HMC).
ArXiV: https://arxiv.org/abs/1708.05239
#statistics
ββWeakly supervised mitosis detection in breast histopathology images using concentric loss
Weakly-supervised mitosis detection in breast histopathology images shows that only using one-click annotation can obtain the best performances on three challenging datasets.
Link: https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118?dgcid=author
#healthcare #medical #CV #cancer #DL
Weakly-supervised mitosis detection in breast histopathology images shows that only using one-click annotation can obtain the best performances on three challenging datasets.
Link: https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118?dgcid=author
#healthcare #medical #CV #cancer #DL
ββLearning to Generalize from Sparse and Underspecified Rewards
Applying reinforcement learning to environments with sparse and underspecified rewards is an ongoing challenge, requiring generalization from limited feedback. Novel method that provides more refined feedback to the agent.
Link: https://ai.googleblog.com/2019/02/learning-to-generalize-from-sparse-and.html
#Google #RL
Applying reinforcement learning to environments with sparse and underspecified rewards is an ongoing challenge, requiring generalization from limited feedback. Novel method that provides more refined feedback to the agent.
Link: https://ai.googleblog.com/2019/02/learning-to-generalize-from-sparse-and.html
#Google #RL
ββπ₯The Blowjob Paper: Scientists Processed 109 Hours of Oral Sex to Develop an AI that Sucks Dick
Running a channel about AI, one can not miss the attempt to apply machine learning to calibrate sex toys performance. Thatβs what researchers did, trying to improve Autoblow 2 device characteristics.
Link: https://motherboard.vice.com/en_us/article/pa9nvv/the-blowjob-paper-scientists-processed-109-hours-of-oral-sex-to-develop-an-ai-that-sucks-dick-autoblow
Paper: https://www.autoblow.com/bjpaper/
#ML #DL #AIeverywhere
Running a channel about AI, one can not miss the attempt to apply machine learning to calibrate sex toys performance. Thatβs what researchers did, trying to improve Autoblow 2 device characteristics.
Link: https://motherboard.vice.com/en_us/article/pa9nvv/the-blowjob-paper-scientists-processed-109-hours-of-oral-sex-to-develop-an-ai-that-sucks-dick-autoblow
Paper: https://www.autoblow.com/bjpaper/
#ML #DL #AIeverywhere
π1
ββFood Discovery with Uber Eats: Recommending for the Marketplace
Another great article from #Uber engeneering team on how they built recommendation engine for #UberEats and what balance they had to maintain.
Link: https://eng.uber.com/uber-eats-recommending-marketplace/
Another great article from #Uber engeneering team on how they built recommendation engine for #UberEats and what balance they had to maintain.
Link: https://eng.uber.com/uber-eats-recommending-marketplace/
Lingvo: A TensorFlow Framework for Sequence Modeling
Release from #GoogleAI: general #tensorflow framework for #NLP.
#Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis.
Link: https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb
Github: https://github.com/tensorflow/lingvo
Release from #GoogleAI: general #tensorflow framework for #NLP.
#Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis.
Link: https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb
Github: https://github.com/tensorflow/lingvo
Medium
Lingvo: A TensorFlow Framework for Sequence Modeling
Posted by Jonathan Shen
ββHow 20th Century Fox uses ML to predict a movie audience
All modern blockbusters seem the same. They have common patterns of more exciting periods following less exciting, rotating emotional moments with action period. It is more about following well-known structure and template to make a well-boxing movie, than about directorβs skill. No suprise, that #ML can be used to predict success of the movie by its trailer.
Link: https://cloud.google.com/blog/products/ai-machine-learning/how-20th-century-fox-uses-ml-to-predict-a-movie-audience
#DL #LAindustry #Google
All modern blockbusters seem the same. They have common patterns of more exciting periods following less exciting, rotating emotional moments with action period. It is more about following well-known structure and template to make a well-boxing movie, than about directorβs skill. No suprise, that #ML can be used to predict success of the movie by its trailer.
Link: https://cloud.google.com/blog/products/ai-machine-learning/how-20th-century-fox-uses-ml-to-predict-a-movie-audience
#DL #LAindustry #Google
π«π·From the subscribers living (or being) in Paris:
Tomorrow, Saturday, 2nd of March, in Paris, come to DS Breakfast at 10h30 at Malongo Cafe, 50 Rue Saint-AndrΓ© des Arts, 75006 Paris.
Tomorrow, Saturday, 2nd of March, in Paris, come to DS Breakfast at 10h30 at Malongo Cafe, 50 Rue Saint-AndrΓ© des Arts, 75006 Paris.
ββDeep Reinforcement Learning for de-novo Drug Design
- 2 networks trained separately:
- generative produces chemically feasible molecule reps
- predictive forecasts desired properties.
- then both trained jointly with the #RL
Link: https://github.com/isayev/ReLeaSE
#PyTorch
- 2 networks trained separately:
- generative produces chemically feasible molecule reps
- predictive forecasts desired properties.
- then both trained jointly with the #RL
Link: https://github.com/isayev/ReLeaSE
#PyTorch
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