Omid Sarfarzadeh and Maysam Asgari-Chenaghlu , we will have a session on #DeepNLP and it’s applications to #SearchEngine and #Chatbot in #Google’s #DevFest, Istanbul. We will be honored to represent adesso Turkey. Thanks to Tufan K. and all adesso Turkey family to provide this chance for us. More information is provided as follows:
#DeepLearning #DeepNLP #NLP #chatbot #SearchEngine #adesso #adessoTurkey

https://devfest.istanbul
https://dfist19.firebaseapp.com/

❇ @AI_Python_EN

Google is now using BERT to improve its core search algorithm. It has been live now for the past couple of days for search queries made in English in the US. A/B tests have shown good results so far. That's huge news especially for people who make money on web traffic! #deeplearning #machinelearning
📝 Article:
https://lnkd.in/d_fwVeg

Google is training graph neural networks to predict smells

#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/344EaAZ

❇️ @AI_Python_EN

Public datasets: weather and climate Google Cloud’s Public Datasets Program :
https://lnkd.in/edhe7wj

#ArtificialIntelligence #Datasets

✴ @AI_Python_EN

When regression models perform poorly, there are typically several reasons. Here are some:

Important variables have been omitted from the model. These may include latent (unobservable) variables, such as socio-economic status.

Errors in the data and missing data.

Measurement error. This is not the same as data errors.

The wrong type of regression was used - there are many kinds of regression models. For example, OLS linear regression was used for count data.

Moderated effects (interactions) were ignored or improperly modeled. A simple example would be when the relationship between age and purchase frequency depends on gender but this has not been accounted for.

Curvilinear effects have been ignored or modeled improperly. For example, the relationship between age and expenditures often does not follow a straight-line path.

Heterogeneity has been ignored or modeled incorrectly. There may be several segments of consumers with different drivers (preferences), for instance.

Our expectations were wrong - the data are low-signal and there's little value to be extracted whatever we do.

Often these errors can be avoided or fixed with little effort, but the person doing the modeling must know how. Unfortunately, some people using regression have little understanding of it.

✴️ @AI_Python_EN

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.

✴️ @AI_Python_EN

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

❇ @AI_Python_EN

ICCV 2019 Best Papers Announced

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


✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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