Regression is a very general term - notice the similarities between neural nets and regression, for instance. Another example is structural equation modeling (SEM). About 40 years ago UCLA professor Peter Bentler and his doctoral student David Weeks showed how SEM could be represented as regression, which reduced the number of required matrices from eight in the traditional LISREL notation to three. Results will be identical. Factor analysis and latent class clustering can also be conceptualized as regression.
I did not have space to mention clustered (multilevel) data. In some cases ignoring hierarchical structure in data can be very consequential and cause us to misinterpret our data. Sometimes different models are required for different levels of the data, as well.

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FUNIT: Few-Shot Unsupervised Image-to-Image Translation

Code on GitHub:
http://bit.ly/2patFxh

Link to Paper:
https://lnkd.in/ekgDy5u

Link to Blog Post:
https://bit.ly/2JIm28n

#DataScience #MachineLearning #ArtificialIntelligence

✴️ @AI_Python_EN

Pytorch-Struct

Fast, general, and tested differentiable structured prediction in PyTorch. By Harvard NLP : https://lnkd.in/e2iGiNa

#PyTorch #DeepLearning #ArtificialIntelligence

✴️ @AI_Python_EN

Evaluating the Factual Consistency of Abstractive Text Summarization
https://lnkd.in/ewFMX8T

#ArtificialIntelligence #DeepLearning #NLP #NaturalLanguageProcessing

✴ @AI_Python_EN

PaperRobot: Incremental Draft Generation of Scientific Ideas
https://lnkd.in/exHGHjW

#ArtificialIntelligence #AI #MachineLearning #DeepLearning

❇ @AI_Python_EN

Look then Listen: Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following

http://bit.ly/2Pn3PR4

#DataScience #MachineLearning #ArtificialIntelligence

✴ @AI_Python_EN

Deep learning hits mathematical theorem proofs

#deeplearning #artificialintelligence

https://arxiv.org/abs/1910.11797

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ICCV 2019 Best Papers Announced

https://medium.com/syncedreview/iccv-2019-best-papers-announced-27a1a21311e1


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PipeDream: A new approach to parallelize DNN training with pipelining

#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/32YHygq

✴️ @AI_Python_EN

Variational Temporal Abstraction

Taesup Kim, Sungjin Ahn, Yoshua Bengio

https://arxiv.org/pdf/1910.00775.pdf

#ActiveLearning #ArtificialIntelligence #DeepLearning

✴️ @AI_Python_EN

Bayesian Deep Learning Benchmarks

GitHub, by the Oxford Applied and Theoretical Machine Learning group

https://github.com/OATML/bdl-benchmarks

#Bayesian #DeepLearning

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Neural Density Estimation and Likelihood-free Inference

George Papamakarios

https://arxiv.org/pdf/1910.13233.pdf

#Bayesian #NeuralDensityEstimation #Inference

✴️ @AI_Python_EN

Credit Risk Analysis Using #MachineLearning and #DeepLearning Models

Lovely paper by Peter Martey Addo, Dominique Guegan and Bertrand Hassani

Code on #Github (it's in #R)

https://github.com/brainy749/CreditRiskPaper

✴️ @AI_Python_EN

Very interesting paper where they solved the three-body problem using deep neural networks in a tremendously more computationally efficient manner. While there is a lot of talk about current deep learning not leading towards human-like intelligence, one must think more deeply as to all the fantastic areas, applications, and fields that current deep learning can be game-changing right now and can lead to a new era of human-machine collaboration.
#deeplearning
#solvingproblems

https://arxiv.org/abs/1910.07291

✴️ @AI_Python_EN

Facebook: Pushing state-of-the-art in 3D content understanding

#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/2JBaYK5

✴️ @AI_Python_EN

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

https://arxiv.org/abs/1910.13148

#MachineLearning #neurips, #NeurIPS2019

✴️ @AI_Python_EN

How 20th Century Fox uses ML to predict a movie audience
Google Cloud Blog
http://bit.ly/2N3I7SC

#AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN