Inferring a person’s looks from the way they speak: from a short input audio segment of a person speaking, the network directly reconstructs an image of the person’s face. Great: Clearly states ethical limits.
Paper: https://arxiv.org/pdf/1905.09773.pdf
Project: https://speech2face.github.io

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Video: #AI, the end of #deeplearning ? #DeepLearning #QuantumMechanics #MachineLearning #CognitiveComputing #RPA #IntelligentAutomation

https://t.co/F5H5qfJeGk
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check this collection of awesome deep Learning resources

https://github.com/frontbench-open-source/Data-Science-Free

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THe best paper from ICLR 2019

https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

Statistics, data science and jazz...

Though many jazz musicians have had extensive formal training in classical music - pianist Andre Previn being one notable example - some have been almost entirely self-taught - guitarist Wes Montgomery being one notable example.

Montgomery developed his own very unusual way of picking. If you're curious about what I mean, there are many videos on YouTube him performing.

He came from a musical family and he definitely had a talent for music. But the way he taught himself to play also had an influence on what he played, much of which was highly original, and his influence on jazz (and rock) continues to this day. He is generally regarded as one of the best guitarists ever.

What does this have to do with statistics and data science? Stats plays a vital role in data science, yet many data scientists are essentially self-taught or have learned from others who were largely self-taught.

I am not the only statistician concerned about misunderstandings and misuse of statistics in data science - Randy Bartlett, for one, has warned of a coming deluge of statistical malpractice. Some would argue that the deluge has arrived.

On the other hand, one wonders if data science will produce an equivalent of Wes Montgomery.

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In a paper published recently, researchers from MIT’s Computer Science & Artificial Intelligence Laboratory have proposed a method for learning a face from audio recordings of that person speaking.

In their architecture, researchers utilize facial recognition pre-trained models as well as a face decoder model which takes as an input a latent vector and outputs an image with a reconstruction.

Paper: https://lnkd.in/fiUBjqh

#machinelearning #deeplearning #speech2face

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Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases
Researchers: Adriano Koshiyama, Nick Firoozye
Paper: https://lnkd.in/fWtth8W
#artificialinteligence
#machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Use of Artificial Intelligence Techniques / Applications in Cyber Defense
Researcher: Ensar Şeker
Paper: http://ow.ly/eqe450uukBx
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Learning Compositional Neural Programs with Recursive Tree Search and Planning

Paper: http://ow.ly/dEaX50uukqv

#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

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Cracking open the black box of automated machine learning

#MachineLearning
https://bit.ly/2HN9ETC

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The code for "Learning Undirected Posteriors by Backpropagation through MCMC" is released. I had lots of fun working on this. The paper comes with in-depth discussion of possible future works, ideal for summer interns😉
paper http://bit.ly/2XkcSDJ
code http://bit.ly/2WetVup

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Bagdasaryan and Shmatikov find that training using private SGD increases error disparities between over and under-represented groups. They blame gradient clipping, which has a larger effect on data points less like the average. An interesting fairness/privacy tradeoff.
Differential Privacy Has Disparate Impact on Model Accuracy.
http://arxiv.org/abs/1905.12101

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Integrating TVM into PyTorch

https://tvm.ai/2019/05/30/pytorch-frontend

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How Computers See Introduction to Convolutional Neural Networks

How do self-driving cars read street signs? How does Facebook automatically tag you in pictures? How does a computer achieve

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Applied Deep Learning for NLP Using PyTorch - PyCon 2019

https://www.youtube.com/watch?v=VBM1u-UIoI0&list=PLPbTDk1hBo3xof51R8pk3kP1BVBuMYP9c&index=1

✴️ @AI_Python_EN

Introduction to Data Science with Python - PyCon 2019

https://www.youtube.com/watch?v=FtaBwrYn-uQ&list=PLPbTDk1hBo3xof51R8pk3kP1BVBuMYP9c&index=7

✴️ @AI_Python_EN

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

https://github.com/RubensZimbres/Repo-2017

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Natural Language Inference with Deep Learning

Slides for the 2019 NAACL tutorial on Natural Language Inference with Deep Learning by Sam Bowman and Xiaodan Zhu: https://lnkd.in/eRicsNj

#artificialintelligence #deeplearning #naturallanguage

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Tutorials on NLP from #NAACL2019. Thanks to the authors for sharing them with us to learn.

Deep Adversarial Learning for NLP - https://lnkd.in/fS9rCEv
Natural Language Inference with Deep Learning - https://lnkd.in/fk6MZea
Transfer Learning in NLP - https://lnkd.in/f6S8R6S

✴️ @AI_Python_EN

3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
Researchers: Gary Storey, Richard Jiang, Shelagh Keogh, Ahmed Bouridane, Chang-Tsun Li
Paper: http://ow.ly/9aMK50uuW68
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Empowering you to use machine learning to get valuable insights from data.

🔥 Implement basic ML algorithms and deep neural networks with PyTorch.
🖥 Run everything on the browser without any set up using Google Colab.
📦 Learn object-oriented ML to code for products, not just tutorials.

Github Link - https://lnkd.in/f8nu8UR

#datascience #data #dataanalysis #ml #machinelearning #deeplearning #ai #artificialintelligence

✴️ @AI_Python_EN