Uplift modeling tutorial

https://habr.com/ru/company/ru_mts/blog/485980/

Code example: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb

TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval

Github: https://github.com/jayleicn/TVRetrieval


PyTorch implementation of MultiModal Transformer (MMT), a method for multimodal (video + subtitle) captioning: https://github.com/jayleicn/TVCaption

Paper: https://arxiv.org/abs/2001.09099v1

📚Fresh book by Nassim Taleb

Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

https://arxiv.org/abs/2001.10488

@ai_machinelearning_big_data

Open Source Differentiable Computer Vision Library for PyTorch

https://kornia.org

Code: https://github.com/kornia/kornia

Paper: https://arxiv.org/abs/1910.02190v2

Project DeepSpeech
A TensorFlow implementation of Baidu's DeepSpeech architecture

Code: https://github.com/mozilla/DeepSpeech

Tensorflow & Pytorch: https://github.com/DemisEom/SpecAugment

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition:

https://arxiv.org/pdf/1904.08779.pdf

Filter Sketch for Network Pruning

Framework of FilterSketch. The top displays the second-order covariance of the pre-trained CNN

Code: https://github.com/lmbxmu/FilterSketch

Paper: https://arxiv.org/abs/2001.08514v1

Torch-Struct: Deep Structured Prediction Library

Code: https://github.com/harvardnlp/pytorch-struct

Paper: https://arxiv.org/abs/2002.00876v1

Fast, general, and tested differentiable structured prediction in PyTorch: https://nlp.seas.harvard.edu/pytorch-struct/

Using ‘radioactive data’ to detect if a data set was used for training

https://ai.facebook.com/blog/using-radioactive-data-to-detect-if-a-data-set-was-used-for-training/

Paper: https://arxiv.org/abs/2002.00937

Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto (2019)

@datascienceiot

🔥Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

SelectionGAN for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance

Code: : https://github.com/Ha0Tang/SelectionGAN

Paper: https://arxiv.org/abs/2002.01048v1

@ai_machinelearning_big_data

CCMatrix: A billion-scale bitext data set for training translation models

CCMatrix is the largest data set of high-quality, web-based bitexts for training translation models

https://ai.facebook.com/blog/ccmatrix-a-billion-scale-bitext-data-set-for-training-translation-models/

Paper: https://arxiv.org/abs/1911.04944

Github: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix

@ai_machinelearning_big_data

Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning

Agent Learning Framework: https://github.com/HorizonRobotics/alf

Github: https://github.com/ruizhaogit/misc

Paper: https://arxiv.org/abs/2002.01963v1

The Annotated Transformer

The Transformer – a model that uses attention to boost the speed with which these models can be trained.

https://nlp.seas.harvard.edu/2018/04/03/attention.html

The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/

Habr: https://habr.com/ru/post/486358/

TensorFlow Lattice: Flexible, controlled and interpretable ML

The library enables you to inject domain knowledge into the learning process through common-sense or policy-driven shape constraints.

https://blog.tensorflow.org/2020/02/tensorflow-lattice-flexible-controlled-and-interpretable-ML.html

Video: https://www.youtube.com/watch?v=ABBnNjbjv2Q&feature=emb_logo

Github: https://github.com/tensorflow/lattice

Recurrent Neural Networks (RNN) with Keras

Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.

https://www.tensorflow.org/guide/keras/rnn

Source code: https://github.com/tensorflow/docs/blob/master/site/en/guide/keras/rnn.ipynb

Habr : https://habr.com/ru/post/487808/

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

Official PyTorch implementation of pre-print Unsupervised Discovery of Interpretable Directions in the GAN Latent

Code: https://github.com/anvoynov/GANLatentDiscovery


Paper: https://arxiv.org/abs/2002.03754

The popularity of machine learning is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.

The PVS-Studio team believes that with machine learning, there are many pitfalls lurking in code analysis tasks.

https://bit.ly/2vqmeV7

Learning to See Transparent Objects

ClearGrasp uses 3 neural networks: a network to estimate surface normals, one for occlusion boundaries (depth discontinuities), and one that masks transparent objects

Google research: https://ai.googleblog.com/2020/02/learning-to-see-transparent-objects.html

Code: https://github.com/Shreeyak/cleargrasp

Dataset: https://sites.google.com/view/transparent-objects

3D Shape Estimation of Transparent Objects for Manipulation: https://sites.google.com/view/cleargrasp