Microsoft’s DoWhy is a Cool Framework for Causal Inference

https://www.kdnuggets.com/2020/08/microsoft-dowhy-framework-causal-inference.html

Github: https://github.com/microsoft/dowhy

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The Hessian Penalty — Official Implementation

It efficiently optimizes the Hessian of your neural network to be diagonal in an input, leading to disentanglement in that input.

https://www.wpeebles.com/hessian-penalty

Github: https://github.com/wpeebles/hessian_penalty

Paper: https://arxiv.org/abs/2008.10599

Video: https://www.youtube.com/watch?v=uZyIcTkSSXA&feature=youtu.be

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Introducing Opacus: A high-speed library for training PyTorch models with differential privacy

https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy/

Github: https://github.com/pytorch/opacus

Differential Privacy Series Part 1 | DP-SGD Algorithm Explained: https://medium.com/pytorch/differential-privacy-series-part-1-dp-sgd-algorithm-explained-12512c3959a3

Top2Vec

Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors.

Github: https://github.com/ddangelov/Top2Vec

Paper: https://arxiv.org/abs/2008.09470v1

Doc2vec: https://radimrehurek.com/gensim/models/doc2vec.html

🃏 Estimate Probabilities Of Card Games

https://becominghuman.ai/estimate-probabilities-of-card-games-3ae50adb75c7

Awsome-domain-adaptation

This repo is a collection of AWESOME things about domain adaptation, including papers, code, etc. Feel free to star and fork.

Github: https://github.com/zhaoxin94/awesome-domain-adaptation

Paper: https://arxiv.org/abs/2009.00155v1

The Little W-Net that Could

State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models.

Github: https://github.com/agaldran/lwnet

Paper: https://arxiv.org/abs/2009.01907v1

KILT: a Benchmark for Knowledge Intensive Language Tasks

All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures.

Github: https://github.com/facebookresearch/KILT

Paper: https://arxiv.org/abs/2009.02252

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🧙‍♂️ How to Create a Cartoonizer with TensorFlow Lite

https://blog.tensorflow.org/2020/09/how-to-create-cartoonizer-with-tf-lite.html

Code: https://github.com/margaretmz/cartoonizer-with-tflite

E2E TFLite Tutorials: https://github.com/ml-gde/e2e-tflite-tutorials

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TorchKGE: Knowledge Graph Embedding in Python and PyTorch

https://torchkge.readthedocs.io/en/latest/

Github: https://github.com/torchkge-team/torchkge

Paper: https://arxiv.org/abs/2009.02963v1

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Understanding the Role of Individual Units in a Deep Neural Network

Examine two types of networks that contain interpretable units: networks trained to classify images of scenes, and networks trained to synthesize images of scenes.

https://dissect.csail.mit.edu/

Github: https://github.com/davidbau/dissect

Website: https://www.pnas.org/content/early/2020/08/31/1907375117

Paper: https://arxiv.org/pdf/2009.05041.pdf

Up Great technology contest READ//ABLE stimulates the development of new approaches to machine learning. It gives great opportunities for NLP developers. Join us for the next AI breakthrough!

Task: to develop an AI product capable of successfully identifying semantic and factual errors in academic essays at the specialist level within the limited time.

Prize: 100 million rubles for each language: Russian or English

Dataset: https://bit.ly/34279IC

A set of text essay files in Russian and English is a usable tool for specialists in the field. The dataset will be replenished.

Info and terms of participation: https://bit.ly/3kGdTBZ

LaSOT

Large-scale Single Object Tracking (LaSOT) aims to provide a dedicated platform for training data-hungry deep trackers as well as assessing long-term tracking performance.

http://vision.cs.stonybrook.edu/~lasot/

Github: https://github.com/HengLan/LaSOT_Evaluation_Toolkit

Dataset: http://vision.cs.stonybrook.edu/~lasot/download.html

Paper: https://arxiv.org/abs/2009.03465

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Rule-Guided Graph Neural Networks for Recommender Systems

Сombination of rule learning and GNNs achieves substantial improvement compared to methods only using either of them

Github: https://github.com/nju-websoft/RGRec

Paper: https://arxiv.org/abs/2009.04104v1

Improving Sparse Training with RigL

https://ai.googleblog.com/2020/09/improving-sparse-training-with-rigl.html

Github: https://github.com/google-research/rigl

Paper: https://arxiv.org/abs/1911.11134

@ai_machinelearning_big_data

Dialog Ranking Pretrained Transformers

It is a set of dialog response ranking models proposed by Microsoft Research NLP Group trained on millions of human feedback data.

Github: https://github.com/golsun/DialogRPT

Paper: https://arxiv.org/abs/2009.06978

Colab: https://colab.research.google.com/drive/1jQXzTYsgdZIQjJKrX4g3CP0_PGCeVU3C?usp=sharing

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MEAL V2

Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks.

Github: https://github.com/szq0214/MEAL-V2

Paper: https://arxiv.org/abs/2009.08453

ImageNet dataset: https://github.com/pytorch/examples/tree/master/imagenet#requirements.

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