MIT/IBM's new AI lets you "paint" with a neural network. Try out the demo here:
http://bit.ly/GANPdemo

✴️ @AI_Python_EN

Some important things to consider in multivariate analysis include:

- Purpose of the modeling
- Background information and relevant theory
- Whether data are cross-sectional or longitudinal
- Are there different levels, e.g., household within city or region
- Which variables to include
- Measurement level for each variable (e.g., continuous, ordinal, nominal)
- Will continuous latent variables (aka factors) be included
- How the variables interrelate, e.g., hypothesized causal relationships
- Random intercepts, slopes
- Will exogenous variables be included, e.g., age affects factors
- Will categorical latent variables (classes) be included
- Will multiple categorical latent variables be needed
- If latent classes included, which parts of the model can vary by class
- Estimation, e.g., MLE or Bayes, numerous options within each

This may sound like gobbledygook to non-statisticians, but all of it can seriously impact decision-making!

✴️ @AI_Python_EN

PyTorchPipe (PTP)

A component-oriented framework for rapid prototyping and training of computational pipelines combining vision and language:
https://lnkd.in/ehJbseR

#PyTorch #NeuralNetworks #DeepLearning

✴️ @AI_Python_EN

One of the hardest problems in #AI is common sense reasoning. This paper by Nazneen Rajani, Bryan McCann, Caiming Xiong +I makes huge progress on this. Powerful, simple and unsupervised method:
https://arxiv.org/abs/1906.02361
Github: https://github.com/salesforce/cos-e
Blog: https://blog.einstein.ai/leveraging-language-models-for-commonsense/

✴️ @AI_Python_EN

Prof. Chris Manning, Director of StanfordAILab & founder of Stanfordnlp, shared inspiring thoughts on research trends and challenges in #computervision and #NLP at #CVPR2019. View full interview:

http://bit.ly/2KR21hO

✴️ @AI_Python_EN

https://lnkd.in/e2awdVx
Not to be confused with (https://lnkd.in/eydGDPu), mmdetection supports all the SOTA detection algorithms.
#pytorch #gpu

✴️ @AI_Python_EN

Using artificial intelligence to better predict severe weather

https://news.psu.edu/story/579356/2019/07/01/research/using-artificial-intelligence-better-predict-severe-weather

✴️ @AI_Python_EN

Pandas Crash Course

Github :https://lnkd.in/gmWAqhz

✴️ @AI_Python_EN

Facebook today announced the open source release of Deep Learning Recommendation Model (DLRM), a state-of-the-art AI model for serving up personalized results in production environments.

DLRM can be found on GitHub, and implementations of the model are available for Facebook’s PyTorch, Facebook’s distributed learning framework Caffe2, and Glow C++.
Link: https://lnkd.in/dEDtai3

Recommendation engines decide a lot of what people see today, whether it’s content on social media sites like Facebook, ecommerce sites like Amazon, or even the first options you see on an Xbox.

Last month, Amazon made its AI for the shopping recommendations system Personalize available on AWS.
FB Research blog for more details:
https://lnkd.in/du2N9Pd

✴️ @AI_Python_EN

Artificial Intelligence: the global landscape of ethics guidelines

Researchers: Anna Jobin, Marcello Ienca, Effy Vayena
Paper: http://ow.ly/mDA430p2R0q

#artificialintelligence #ai #ml #machinelearning #bigdata #deeplearning #technology #datascience

✴️ @AI_Python_EN

Natural-Language-Processing: 8 week course material

This is the curriculum for "Learn Natural Language Processing" by Siraj Raval on Youtube
https://github.com/llSourcell/Learn-Natural-Language-Processing-Curriculum/blob/master/README.md

✴️ @AI_Python_EN

The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification. Khaled Koutini, Hamid Eghbal-zadeh, Matthias Dorfer, and Gerhard Widmer
http://arxiv.org/abs/1907.01803

✴️ @AI_Python_EN

Data at its lowest form of aggregation is not always most useful, and it's very easy to waste time, energy and computing resources trying to model and predict tiny patterns and fluctuations which are, for all intents and purposes, random.

Some big data are unnecessary big, and more value at less cost could have been extracted had the data been intelligently aggregated prior to the modeling.

There is also the habit of using gigantic data files when samples would have sufficed. :-)

✴️ @AI_Python_EN

If you're interested in transfer learning in NLP, you definitely need to check out Daniel Pressel's Github repository. He recently gave a keynote and tutorial at the International Summer School on Deep Learning, 2019 in Gdansk, Poland. The slides are online and really amazing and also he uploaded the tutorial. Colab notebooks are also provided. Check it out! #deeplearning #machinelearning

📊 Slides: https://lnkd.in/dttQJ8P
🔤 Github: https://lnkd.in/d_fscpV

✴️ @AI_Python_EN

https://lnkd.in/fJkMyHM

Facebook AI Research (FAIR) and NYU School of Medicine’s [Center for Advanced Imaging Innovation and Research (CAI²R)](http://www.cai2r.net/) are sharing new open source tools and data as part of [fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x](https://lnkd.in/fQxPgMv) (3 minutes instead of 30 minutes).
✴️ @AI_Python_EN

Learning from unlabelled videos is key to scaling computer vision models. Our method learns state-of-the-art pose detectors given only examples of skeletons or facial landmarks. It further enables image generation and manipulation conditioned on landmarks.
https://arxiv.org/abs/1907.02055

✴️ @AI_Python_EN

Benchmarking Model-Based Reinforcement Learning"
https://arxiv.org/pdf/1907.02057.pdf
summarized existing MBRL algorithms into three types, and benchmarked 14 algorithms on 18 environments (CartPole to Humanoid).
Project & Code http://www.cs.toronto.edu/~tingwuwang/mbrl.html

✴️ @AI_Python_EN

bit.ly/2JeYsQr
Demystifying the Math Behind Neural Nets — learn how #NeuralNetworks Learn, with an implementation demonstrated one step at a time:
http://bit.ly/2JgmGKh

✴️ @AI_Python_EN

DeepMind:
For anyone interested in constrained optimisation with DL models (e.g. as in https://arxiv.org/abs/1810.00597 ), we just released a few handy tools to deal with inequality constraints for Sonnet (http://tiny.cc/a9va9y ).

✴️ @AI_Python_EN

New paper on much faster RL by learning graph representations of the world http://arxiv.org/abs/1907.00664 ! World graphs capture the structure of the world and can be used to focus exploration:
http://www-personal.umich.edu/~shangw/world_graph.html

✴️ @AI_Python_EN

Training on only synthetic data, our new paper gets near the SOTA on the real-world 3DPW human pose dataset by focusing on motion. Simulation is underappreciated for representation learning; you just have to keep the nets from overfitting to the domain.
https://arxiv.org/abs/1907.02499

✴️ @AI_Python_EN