Introducing TF-Coder, a tool that writes tricky TensorFlow expressions for you

https://blog.tensorflow.org/2020/08/introducing-tensorflow-coder-tool.html

Paper: https://arxiv.org/abs/2003.09040

Code: https://github.com/google-research/tensorflow-coder

Colab: https://colab.research.google.com/github/google-research/tensorflow-coder/blob/master/TF-Coder_Colab.ipynb

👄 Wav2Lip: Accurately Lip-syncing Videos In The Wild

Lip-sync videos to any target speech with high accuracy. Try our interactive demo.

Github: https://github.com/Rudrabha/Wav2Lip

Paper: https://arxiv.org/abs/2008.10010

Interactive Demo: https://bhaasha.iiit.ac.in/lipsync/

Colab: https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

🧙‍♂️ 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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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