Torch vs Theano vs TensorFlow vs Keras

☞ https://morioh.com/p/a80813c4a01c

#ai #deeplearning

❇️ @AI_Python_EN

"...can we say now, finally, that computers are as powerful as the human brain? No. Focusing on raw computing power misses the point entirely. Speed alone won’t give us AI. Running a poorly designed algorithm on a faster computer doesn’t make the algorithm better; it just means you get the wrong answer more quickly. (And with more data there are more opportunities for wrong answers!)

The principal effect of faster machines has been to make the time for experimentation shorter, so that research can progress more quickly. It’s not hardware that is holding AI back; it’s software. We don’t yet know how to make a machine really intelligent—even if it were the size of the universe...

Turing himself proved that some problems are undecidable by any computer: the problem is well defined, there is an answer, but there cannot exist an algorithm that always finds that answer...

The machine may be far more capable than us, but it will still be far from perfectly rational."

Stuart Russell in "Human Compatible"

❇️ @AI_Python_EN

Predicting survival from colorectal cancer histology slides using #deeplearning

1. They conducted the study because:
• Colorectal cancer (CRC) is a common disease with a variable clinical course, and there is a high clinical need to more accurately predict the outcome of individual patients.
• For almost every CRC patient, histological slides of tumor tissue are routinely available.
• Deep learning can be used to extract information from very complex images, and we hypothesized that deep learning can predict clinical outcome directly from histological images of CRC.

2. What did the researchers do and find?
• We trained a deep neural network to identify different tissue types & demonstrated that it can decompose complex tissue into its constituent parts and thereby showed that this score improves survival prediction compared to the SOTA avaiable.

3. Conclusion
• Deep learning is an inexpensive tool to predict the clinical course of CRC patients based on ubiquitously available histological images.
• Prospective validation studies are needed to firmly establish this biomarker for routine clinical use.

☞ Link to research

#healthcare #AI #machinelearning

❇️ @AI_Python_EN

The State of Transfer Learning in NLP

http://ruder.io/state-of-transfer-learning-in-nlp/

#TransferLearning #NaturalLanguageProcessing
#NLP

❇️ @AI_Python_EN

Autoencoders for Image Reconstruction in Python and Keras

https://stackabuse.com/autoencoders-for-image-reconstruction-in-python-and-keras/


❇️ @AI_Python_EN

DeepPrivacy model for making people on photoes unrecognizable (by humans)


ArXiV: https://arxiv.org/pdf/1909.04538.pdf

#MaskRCNN #DeepPrivacy #CV #DL

❇️ @AI_Python_EN

Self-supervised QA from Facebook AI

The researchers from Facebook AI published a paper with the results of exploring the idea of unsupervised extractive question answering and the following training of the supervised question answering model. This approach achieves 56.41F1 on SQuAD2 dataset.

Original paper:
https://research.fb.com/wp-content/uploads/2019/07/Unsupervised-Question-Answering-by-Cloze-Translation.pdf?
Code for experiments:
https://github.com/facebookresearch/UnsupervisedQA


#NLP #BERT #FacebookAI #SelfSupervised

❇️ @AI_Python_EN

1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)
https://deepai.org/publication/1-point-ransac-for-circular-motion-estimation-in-computed-tomography-ct

by Mikhail O. Chekanov et al.
#Statistics #Estimator

❇️ @AI_Python_EN

5 types of #machinelearning algorithms you should know
Model development is not one-size-fits-all -- there are different types of #machinelearning algorithm

https://www.7wdata.be/big-data/5-types-of-machine-learning-algorithms-you-should-know/

❇️ @AI_Python_EN

Deep Learning:
http://course.fast.ai
NLP:
http://bit.ly/fastai-nlp
Comp Linear Algebra:  
http://github.com/fastai/numerical-linear-algebra
Bias, Ethics, & AI:
http://fast.ai/topics/#ai-in-society
Debunk Pipeline Myth:
http://bit.ly/not-pipeline
AI Needs You:
http://bit.ly/rachel-TEDx
Ethics Center: 
http://bit.ly/USF-CADE

❇️ @AI_Python_EN

Simple, Scalable Adaptation for Neural Machine Translation

Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. Researchers from Google propose a simple yet efficient approach for adaptation in #NMT. Their proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously.

Guess it can be applied not only in #NMT but in many other #NLP, #NLU and #NLG tasks.

Paper: https://arxiv.org/pdf/1909.08478.pdf

#BERT

❇️ @AI_Python_EN

Communication-based Evaluation for Natural Language Generation (#NLG) that's dramatically out-performed standard n-gram-based methods.

Have you ever think that n-gram overlap measures like #BLEU or #ROUGE is not good enough for #NLG evaluation and human based evaluation is too expensive? Researchers from Stanford University also think so. The main shortcoming of #BLEU or #ROUGE methods is that they fail to take into account the communicative function of language; a speaker's goal is not only to produce well-formed expressions, but also to convey relevant information to a listener.

Researchers propose approach based on color reference game. In this game, a speaker and a listener see a set of three colors. The speaker is told one color is the target and tries to communicate the target to the listener using a natural language utterance. A good utterance is more likely to lead the listener to select the target, while a bad utterance is less likely to do so. In turn, effective metrics should assign high scores to good utterances and low scores to bad ones.

Paper: https://arxiv.org/pdf/1909.07290.pdf
Code: https://github.com/bnewm0609/comm-eval

#NLP #NLU

❇️ @AI_Python_EN

Statistician: How might you analyze this data

Data scientist: Definitely start with a neural net

Statistician:

Alright, fine, start off with a multiple regression with every possible covariate, look at the t-tests and remove those that aren't significant.
Statistician: have you tried PCA?

Data scientist: no, but I'm using a linearly-activated autoencoder, and that seems to work pretty well.

Statistician: ...

❇ @AI_Python
✴ @AI_Python_EN

Transformers working for RL! Two simple modifications: move layer-norm and add gating creates GTrXL: an incredibly stable and effective architecture for integrating experience through time in RL.
https://arxiv.org/abs/1910.06764

❇️ @AI_Python
✴️ @AI_Python_EN

Uncertainty Quantification in Deep Learning

https://www.inovex.de/blog/uncertainty-quantification-deep-learning/

Most of the world’s text is not in English. We are releasing MultiFiT to train and fine-tune language models efficiently in any language.
Post:
http://nlp.fast.ai/classification/2019/09/10/multifit.html
Paper:
https://arxiv.org/abs/1909.04761

✴ @AI_Python_EN

Did you run any experiments in XNLI? Also curious how it compares to XLM. Also, shameless plug for the cross-lingual QA dataset we just released, MLQA
https://github.com/facebookresearch/MLQA - could be a great testbed for models like this

❇ @AI_Python_EN

XNLI and MLQA needs bidirectional context, and multifit is unidirectional since it uses casual language modeling and RNNs. But it is on the todo list just below of training multifit in zeroshoot scenario with XLM as a teacher model.

❇ @AI_Python_EN

Does the brain do backpropagation? CAN Public Lecture - Geoffrey Hinton

https://www.youtube.com/watch?v=qIEfJ6OBGj8

❇ @AI_Python_EN

Netflix Open-sourcing Polynote: an IDE-inspired polyglot notebook

#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/2N9m8qe


❇️ @AI_Python_EN

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