ββFast video object segmentation with Spatio-Temporal GANs
Spatio-Temporal GANs to the Video Object Segmentation task, allowing to run at 32 FPS without fine-tuning.
#FaSTGAN #GAN #Segmentation #videomining #CV #DL
Spatio-Temporal GANs to the Video Object Segmentation task, allowing to run at 32 FPS without fine-tuning.
#FaSTGAN #GAN #Segmentation #videomining #CV #DL
ββStep Change Improvement in Molecular Property Prediction with PotentialNet
Paper on a significant improvement in ability to predict molecular properties in drug design. #ML algorithms are getting better and better than classical methods.
Link: https://medium.com/@pandelab/step-change-improvement-in-molecular-property-prediction-with-potentialnet-f431ffa32a2c
#drugsdesign #biolearning #healthcare
Paper on a significant improvement in ability to predict molecular properties in drug design. #ML algorithms are getting better and better than classical methods.
Link: https://medium.com/@pandelab/step-change-improvement-in-molecular-property-prediction-with-potentialnet-f431ffa32a2c
#drugsdesign #biolearning #healthcare
ββOpen-sourcing PyTorch-BigGraph for faster embeddings of extremely large graphs
PyTorch-BigGraphβ a tool that for faster and easier producing graph embeddings for extremely large graphs. Outputs high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.
Link: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/
Github: https://github.com/facebookresearch/PyTorch-BigGraph
#PyTorch #Facebook #OpenSourceRelease #Embeddings #GraphLearning
PyTorch-BigGraphβ a tool that for faster and easier producing graph embeddings for extremely large graphs. Outputs high-quality embeddings without specialized computing resources like GPUs or huge amounts of memory.
Link: https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/
Github: https://github.com/facebookresearch/PyTorch-BigGraph
#PyTorch #Facebook #OpenSourceRelease #Embeddings #GraphLearning
Note on moving from being frequntist to being bayesian
Beautiful article about core differences and personal experience.
Link: http://www.fharrell.com/post/journey/
#InBayesWeTrust #BvsF
Beautiful article about core differences and personal experience.
Link: http://www.fharrell.com/post/journey/
#InBayesWeTrust #BvsF
Statistical Thinking
My Journey from Frequentist to Bayesian Statistics β Statistical Thinking
This is the story of what influenced me to become a Bayesian statistician after being trained as a classical frequentist statistician, and practicing only that mode of statistics for many years.
ββAI talent flow map
Very interesting map of the flow of students between countries on the 2019 Global AI Talent report.
Link: https://jfgagne.ai/talent-2019/
Very interesting map of the flow of students between countries on the 2019 Global AI Talent report.
Link: https://jfgagne.ai/talent-2019/
ββDeep Neural Networks Improve Radiologistsβ Performance in Breast Cancer Screening
Paper claims model to perform better than human.
ArXiV: https://arxiv.org/abs/1903.08297
GitHub (code & models): https://github.com/nyukat/breast_cancer_classifier
#BreastCaner #healthcare #DL #CV
Paper claims model to perform better than human.
ArXiV: https://arxiv.org/abs/1903.08297
GitHub (code & models): https://github.com/nyukat/breast_cancer_classifier
#BreastCaner #healthcare #DL #CV
ββπ₯DeepMind released dataset for training algebraic perception
Mathematics Dataset is a large-scale extendable dataset of mathematical questions, for training (and evaluating the abilities of) neural models that can solve algebraic problems (reason algebraically).
ArXiV: https://arxiv.org/abs/1904.01557
GitHub: https://github.com/deepmind/mathematics_dataset
Mathematics Dataset is a large-scale extendable dataset of mathematical questions, for training (and evaluating the abilities of) neural models that can solve algebraic problems (reason algebraically).
ArXiV: https://arxiv.org/abs/1904.01557
GitHub: https://github.com/deepmind/mathematics_dataset
ββπ€Handl: New dataset labeling tool release
Handl is a tool to label and manage data for machine learning. It employs 25k qualified crowdworkers who help tech companies to deal with data preparation and get paid for it. Consensus algorithm ensures the quality of labeling for any type of data β images, texts, and sounds.
#Handl was released today at Product Hunt, so developers might benefit from community upvotes, please consider supporting such useful tool on Product Hunt.
Link: https://handl.ai
Product Hunt url: https://www.producthunt.com/posts/handl-3
#handl #machinelearning #ai #data #datalabeling
Handl is a tool to label and manage data for machine learning. It employs 25k qualified crowdworkers who help tech companies to deal with data preparation and get paid for it. Consensus algorithm ensures the quality of labeling for any type of data β images, texts, and sounds.
#Handl was released today at Product Hunt, so developers might benefit from community upvotes, please consider supporting such useful tool on Product Hunt.
Link: https://handl.ai
Product Hunt url: https://www.producthunt.com/posts/handl-3
#handl #machinelearning #ai #data #datalabeling
Scalable Muscle-actuated Human Simulation and Control
Spectacular work from Seoul National University on motion-mimicing DeepRL with a comprehensive full-body musculoskeletal model with 346 muscles, including predictive simulations of post-operative gaits.
YouTube: https://www.youtube.com/watch?v=a3jfyJ9JVeM
Project link: http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm
#SIGGRAPH2019 #RL
Spectacular work from Seoul National University on motion-mimicing DeepRL with a comprehensive full-body musculoskeletal model with 346 muscles, including predictive simulations of post-operative gaits.
YouTube: https://www.youtube.com/watch?v=a3jfyJ9JVeM
Project link: http://mrl.snu.ac.kr/research/ProjectScalable/Page.htm
#SIGGRAPH2019 #RL
YouTube
Scalable Muscle-actuated Human Simulation and Control(SIGGRAPH 2019)
Seunghwan Lee, Kyoungmin Lee, Moonseok Park, and Jehee Lee,
Scalable Muscle-actuated Human Simulation and Control
ACM Transactions on Graphics (SIGGRAPH 2019), Volume 37, Article 73
Github Page : https://github.com/lsw9021/MASS
Project Page : http://mrβ¦
Scalable Muscle-actuated Human Simulation and Control
ACM Transactions on Graphics (SIGGRAPH 2019), Volume 37, Article 73
Github Page : https://github.com/lsw9021/MASS
Project Page : http://mrβ¦
Make Trump Sing Again
Generated by a Trump TTS model trained based off the paper "Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis", where given a reference audio the model will try to replicate that style.
ArXiV: https://arxiv.org/pdf/1803.09017.pdf
Youtube: https://youtu.be/3rgAVT8b4fw
#tts #song #speech #DL
Generated by a Trump TTS model trained based off the paper "Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis", where given a reference audio the model will try to replicate that style.
ArXiV: https://arxiv.org/pdf/1803.09017.pdf
Youtube: https://youtu.be/3rgAVT8b4fw
#tts #song #speech #DL
DGC-Net: Dense Geometric Correspondence network
Paper addresses the challenge of dense pixel correspondence estimation between two images. Practically, this means that it is about comparing different views of one object, which is very important to make #CV more robust.
ArXiV: https://arxiv.org/abs/1810.08393
Github: https://github.com/AaltoVision/DGC-Net
YouTube: https://www.youtube.com/watch?v=xnQMEr4FbHE
Project page: https://aaltovision.github.io/dgc-net-site/
#CNN #DL
Paper addresses the challenge of dense pixel correspondence estimation between two images. Practically, this means that it is about comparing different views of one object, which is very important to make #CV more robust.
ArXiV: https://arxiv.org/abs/1810.08393
Github: https://github.com/AaltoVision/DGC-Net
YouTube: https://www.youtube.com/watch?v=xnQMEr4FbHE
Project page: https://aaltovision.github.io/dgc-net-site/
#CNN #DL
arXiv.org
DGC-Net: Dense Geometric Correspondence Network
This paper addresses the challenge of dense pixel correspondence estimation
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently...
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently...
Forwarded from EarthML
Automatic feature selection:
EGU is still ongoing, but I am burning to share with you some of my findings already!
Research group in University of Lausanne developed a pretty promising algorithm for automatic feature selection based on General Regression Neural Network (GRNN, also known as Nadaraya-Watson Estimator). The idea behind is pretty simple and therefor powerful - why won't we build the simplest network that can train really fast and brute force all possible combination of features to check how they affect accuracy, learning rate etc and than select the best performing once.
Here is Python implementation on GitHub: https://github.com/federhub/pyGRNN
Also, check their poster: https://github.com/federhub/pyGRNN/blob/master/EGU2019_FS_using_simple_and_efficient_ML_models.pdf
Stay tuned, subscribe and share!
xoxo
EGU is still ongoing, but I am burning to share with you some of my findings already!
Research group in University of Lausanne developed a pretty promising algorithm for automatic feature selection based on General Regression Neural Network (GRNN, also known as Nadaraya-Watson Estimator). The idea behind is pretty simple and therefor powerful - why won't we build the simplest network that can train really fast and brute force all possible combination of features to check how they affect accuracy, learning rate etc and than select the best performing once.
Here is Python implementation on GitHub: https://github.com/federhub/pyGRNN
Also, check their poster: https://github.com/federhub/pyGRNN/blob/master/EGU2019_FS_using_simple_and_efficient_ML_models.pdf
Stay tuned, subscribe and share!
xoxo
GitHub
GitHub - federhub/pyGRNN: Python implementation of General Regression Neural Network (Nadaraya-Watson Estimator). A Feature Selectionβ¦
Python implementation of General Regression Neural Network (Nadaraya-Watson Estimator). A Feature Selection module based on GRNN is also provided - federhub/pyGRNN
ββAutodesk claims to use GANs to design a chair.
First-of-its-kind chair from Philippe Starck and Kartell. Imagined by a human and cocreated with intelligent generative design
Link: https://adsknews.autodesk.com/news/starck-intelligent-generative-design
Generative design explanation link: https://www.autodesk.com/solutions/generative-design
#Autodesk #generativedesign
First-of-its-kind chair from Philippe Starck and Kartell. Imagined by a human and cocreated with intelligent generative design
Link: https://adsknews.autodesk.com/news/starck-intelligent-generative-design
Generative design explanation link: https://www.autodesk.com/solutions/generative-design
#Autodesk #generativedesign
ββGoogleβs progress on AutoML
Hint: itβs beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
Hint: itβs beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
ββHow is Uber predicting demand, surge and where will be high demand area.
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
General talk on who makes the choice now: machine or human
Discussion based on the book Β«A Humanβs Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in ControlΒ».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Discussion based on the book Β«A Humanβs Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in ControlΒ».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Knowledge at Wharton
Who Made That Decision: You or an Algorithm?
Algorithms now make lots of decisions, but they have their own biases, writes Whartonβs Kartik Hosanagar in his new book. β¦Read More
Text Mining 101: Topic Modeling
General intro into #LDA and #textrank
Link: https://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
#nlp
General intro into #LDA and #textrank
Link: https://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
#nlp
ββNew paper on #osteoarthritis progression prediction
Link: https://arxiv.org/abs/1904.06236
GitHub (model & code): https://github.com/MIPT-Oulu/OAProgression
#biolearning #healthcare #ML
Link: https://arxiv.org/abs/1904.06236
GitHub (model & code): https://github.com/MIPT-Oulu/OAProgression
#biolearning #healthcare #ML
