We are open-sourcing Pythia, a deep learning platform to support multitasking for vision and language tasks. With Pythia, researchers can more easily build, reproduce, and benchmark AI models.
https://code.fb.com/ai-research/pythia/
https://code.fb.com/ai-research/pythia/
Engineering at Meta
Releasing Pythia for vision and language multimodal AI models
Pythia is a new open source deep learning framework that enables researchers to quickly build, reproduce, and benchmark AI models.
NVIDIA DLSS technology is now live in Anthem!
Use the power of deep learning and AI to help players achieve smooth frame rates with graphically-intensive settings.
Learn more → https://www.nvidia.com/en-us/geforce/news/anthem-nvidia-dlss-highlights/
Use the power of deep learning and AI to help players achieve smooth frame rates with graphically-intensive settings.
Learn more → https://www.nvidia.com/en-us/geforce/news/anthem-nvidia-dlss-highlights/
NVIDIA GeForce
Anthem Adds NVIDIA DLSS and NVIDIA Highlights In New Update
Get up to 40% faster performance on GeForce RTX graphics cards, and automatically capture your best gameplay moments for quick and easy sharing online.
Course material for STAT 479: Deep Learning (SS 2019) course at University Wisconsin-Madison
https://github.com/rasbt/stat479-deep-learning-ss19/blob/master/L02_dl-history/L02_dl-history_slides.pdf
https://github.com/rasbt/stat479-deep-learning-ss19/blob/master/L02_dl-history/L02_dl-history_slides.pdf
GitHub
rasbt/stat479-deep-learning-ss19
Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison - rasbt/stat479-deep-learning-ss19
ML and agriculture https://medium.com/sciforce/machine-learning-in-agriculture-applications-and-techniques-6ab501f4d1b5
Medium
Machine Learning in Agriculture: Applications and Techniques
Recently we have discussed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of…
Notes on machine learning https://www.holehouse.org/mlclass/
PREdiction Conditioned on Goals in Visual Multi-agent Settings
https://sites.google.com/view/precog
paper https://arxiv.org/pdf/1905.01296.pdf
https://sites.google.com/view/precog
paper https://arxiv.org/pdf/1905.01296.pdf
Google
PRECOG
25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
Open Data Science - Your News Source for AI, Machine Learning & more
25 Excellent Machine Learning Open Datasets
Looking to work on some data, but can't collect your own? Here are 25 helpful machine learning open datasets to use today!
Graph Convolutional Networks for Geometric Deep Learning
https://towardsdatascience.com/graph-convolutional-networks-for-geometric-deep-learning-1faf17dee008
https://towardsdatascience.com/graph-convolutional-networks-for-geometric-deep-learning-1faf17dee008
Medium
Graph Convolutional Networks for Geometric Deep Learning
Graph Learning and Geometric Deep Learning — Part 2
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin, Duke University
https://www.nature.com/articles/s42256-019-0048-x
ArXiv Version:
https://arxiv.org/pdf/1811.10154
#artificialintelligence #explainableai
#blackbox
Cynthia Rudin, Duke University
https://www.nature.com/articles/s42256-019-0048-x
ArXiv Version:
https://arxiv.org/pdf/1811.10154
#artificialintelligence #explainableai
#blackbox
Nature
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Nature Machine Intelligence - There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black...
The network architecture of the human brain is modularly encoded in the genome
Bertolero et al.: https://arxiv.org/abs/1905.07606
#Neurons #Cognition #Genomics #Populations #Evolution
Bertolero et al.: https://arxiv.org/abs/1905.07606
#Neurons #Cognition #Genomics #Populations #Evolution
https://www.wired.com/story/googles-ai-guru-computers-think-more-like-brains/ Google AI #GeoffreyHinton
WIRED
Google’s AI Guru Wants Computers to Think More Like Brains
Google's top AI researcher, Geoff Hinton, discusses a controversial Pentagon contract, a shortage of radical ideas, and fears of an "AI winter."
Applied Machine Learning course of Columbia University Spring 2018
https://www.cs.columbia.edu/~amueller/comsw4995s18/schedule/
https://www.cs.columbia.edu/~amueller/comsw4995s18/schedule/
Andreas C. Müller - Associate Research Scientist
COMS W4995 Applied Machine Learning Spring 2018 - Schedule
Website of Associate Research Scientist Andreas C. Mueller - Columbia University
Bloomberg talks about FAIR's robotics research effort.
https://www.google.com/amp/s/www.bloomberg.com/amp/news/articles/2019-05-20/facebook-s-robotic-arms-and-legs-are-learning-faster-than-ever
https://www.google.com/amp/s/www.bloomberg.com/amp/news/articles/2019-05-20/facebook-s-robotic-arms-and-legs-are-learning-faster-than-ever
Troubleshooting Deep Neural Networks
A Field Guide to Fixing Your Model
https://josh-tobin.com/troubleshooting-deep-neural-networks
#ArtificialIntelligence #DeepLearning #NeuralNetworks
A Field Guide to Fixing Your Model
https://josh-tobin.com/troubleshooting-deep-neural-networks
#ArtificialIntelligence #DeepLearning #NeuralNetworks
Josh-Tobin
Troubleshooting Deep Neural Networks
A Field Guide to Fixing Your Model
TensorWatch: A debugging and visualization tool designed for deep learning
https://github.com/microsoft/tensorwatch
https://github.com/microsoft/tensorwatch
GitHub
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
Debugging, monitoring and visualization for Python Machine Learning and Data Science - microsoft/tensorwatch
The Best and Most Current of Modern Natural Language Processing
Blog by Victor Sanh: https://medium.com/huggingface/the-best-and-most-current-of-modern-natural-language-processing-5055f409a1d1
#NaturalLanguageProcessing #MachineLearning #NLP #DeepLearning #Research
Blog by Victor Sanh: https://medium.com/huggingface/the-best-and-most-current-of-modern-natural-language-processing-5055f409a1d1
#NaturalLanguageProcessing #MachineLearning #NLP #DeepLearning #Research
Medium
🌻 The Best and Most Current of Modern Natural Language Processing
Which papers can I read to catch up with the latest trends in modern Natural Language Processing?
Human Visual Understanding for Cognition and Manipulation -- A primer for the roboticist
Hjelm et al.: https://arxiv.org/abs/1905.05272
#Robotics #Neurons #Cognition
Hjelm et al.: https://arxiv.org/abs/1905.05272
#Robotics #Neurons #Cognition
arXiv.org
Human Visual Understanding for Cognition and Manipulation -- A...
Robotic research is often built on approaches that are motivated by insights
from self-examination of how we interface with the world. However, given
current theories about human cognition and...
from self-examination of how we interface with the world. However, given
current theories about human cognition and...
25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
Open Data Science - Your News Source for AI, Machine Learning & more
25 Excellent Machine Learning Open Datasets
Looking to work on some data, but can't collect your own? Here are 25 helpful machine learning open datasets to use today!
A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.
https://news.mit.edu/2019/machine-learning-amino-acids-protein-function-0322
https://news.mit.edu/2019/machine-learning-amino-acids-protein-function-0322
MIT News | Massachusetts Institute of Technology
Model learns how individual amino acids determine protein function
A model from MIT researchers “learns” vector embeddings of each amino acid position in a 3-D protein structure, which can be used as input features for machine-learning models to predict amino acid segment functions for drug development and biological research.