Knover allows researchers and developers efficient training/inference of large-scale dialogue generation models.

Github: https://github.com/PaddlePaddle/Knover

Paper: https://arxiv.org/abs/2102.02096v1

@ai_machinelearning_big_data

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

Github: https://github.com/yuval-alaluf/SAM

Paper: https://arxiv.org/abs/2102.02754v1

@ai_machinelearning_big_data

MeInGame: Create a Game Character Face from a Single Portrait

Github : https://github.com/FuxiCV/MeInGame

Paper: https://arxiv.org/abs/2102.02371v1

Demo: https://www.youtube.com/watch?v=597cvKOegfE&feature=youtu.be&ab_channel=EdwardLin

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🎨 Colorization Transformer by Google

💻 Github: https://github.com/google-research/google-research/tree/master/coltran

📝 Paper: https://arxiv.org/abs/2102.04432v1

🌐 Pre-trained model: https://console.cloud.google.com/storage/browser/gresearch/coltran;tab=objects?pli=1&prefix=&forceOnObjectsSortingFiltering=false

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🔊 Deep Perceptual Audio Metric (DPAM)

Github : https://github.com/pranaymanocha/PerceptualAudio

Paper: https://arxiv.org/abs/2102.05109v1

Website: https://pixl.cs.princeton.edu/pubs/Manocha_2021_CCL/index.php

Video: https://www.youtube.com/watch?v=yOceeut_4Gg&ab_channel=PranayManocha

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🚀 High-Performance Large-Scale Image Recognition Without Normalization

Github: https://github.com/deepmind/deepmind-research/tree/master/nfnets

Paper: https://arxiv.org/abs/2102.06171v1

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🧪 Alchemy: A structured task distribution for meta-reinforcement learning

Deepmind: https://deepmind.com/research/publications/alchemy

Github: https://github.com/deepmind/dm_alchemy

Paper: https://arxiv.org/abs/2102.02926

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👁 18 All-Time Classic Open Source Computer Vision Projects for Beginners

https://www.analyticsvidhya.com/blog/2020/09/18-open-source-computer-vision-projects-beginners/

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💪 These Neural Networks Have Superpowers!

 Github: https://github.com/CompVis/taming-transformers

Taming Transformers for High-Resolution Image Synthesis: https://compvis.github.io/taming-transformers/

Paper: https://arxiv.org/abs/2012.09841

Video: https://www.youtube.com/watch?v=o7dqGcLDf0A&ab_channel=TwoMinutePapers

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📍TransGAN: Two Transformers Can Make One Strong GAN

Github: https://github.com/VITA-Group/TransGAN

Paper: https://arxiv.org/abs/2102.07074

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🕹 Mastering Atari with Discrete World Models

Github: https://github.com/danijar/dreamerv2

Google research: https://ai.googleblog.com/2021/02/mastering-atari-with-discrete-world.html

Paper: https://arxiv.org/abs/2010.02193

@ai_machinelearning_big_data

Unbiased Teacher for Semi-Supervised Object Detection

Github: https://github.com/facebookresearch/unbiased-teacher

Paper: https://arxiv.org/abs/2102.09480

Project: https://ycliu93.github.io/projects/unbiasedteacher.html

@ai_machinelearning_big_data

🔥 Model Search by Google

Automatically build and deploy state-of-the-art machine learning models on structured data.

Github: https://github.com/google/model_search

Paper: https://pdfs.semanticscholar.org/1bca/d4cdfbc01fbb60a815660d034e561843d67a.pdf

Project: https://cloud.google.com/automl-tables

@ai_machinelearning_big_data

🚀 DALL-E Zero-Shot Text-to-Image Generation

Github: https://github.com/openai/DALL-E

Paper: https://arxiv.org/abs/2102.12092

OpenAi: https://openai.com/blog/dall-e/

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When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute

Github: https://github.com/asappresearch/sru

Paper: https://arxiv.org/abs/2102.12459v1

Project: https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/

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XLA: Optimizing Compiler for Machine Learning

Tensorflow: https://www.tensorflow.org/xla

XLA Architecture: https://www.tensorflow.org/xla/architecture

Github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla

Code: https://www.tensorflow.org/xla/tutorials/jit_compile

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⭐ CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

Github: https://github.com/Davidzhangyuanhan/CelebA-Spoof

Paper: https://arxiv.org/abs/2102.12642v2

Dataset: https://drive.google.com/drive/folders/1OW_1bawO79pRqdVEVmBzp8HSxdSwln_Z

Video: https://www.youtube.com/watch?v=A7XjSg5srvI&t=4s&ab_channel=YuanhanZhang

@ai_machinelearning_big_data

CogDL: An Extensive Research Toolkit for ​Deep Learning on Graphs

http://keg.cs.tsinghua.edu.cn/cogdl/

Github: https://github.com/THUDM/cogdl

Paper: https://arxiv.org/abs/2103.00959

Dateset: https://github.com/THUDM/cogdl/blob/master/cogdl/datasets/README.md

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🤖 A Text-to-Speech Transformer in TensorFlow 2

Github: https://github.com/as-ideas/TransformerTTS

Paper: https://arxiv.org/abs/2103.00993v1

Samples: https://as-ideas.github.io/TransformerTTS/

@ai_machinelearning_big_data

🧠 Multimodal Neurons in Artificial Neural Networks

https://openai.com/blog/multimodal-neurons/

Github: https://github.com/openai/CLIP-featurevis

Paper: https://distill.pub/2021/multimodal-neurons/

@ai_machinelearning_big_data

Register for the International Data Analysis Olympiad (IDAO-2021)! The registration continues until March 12.

This year, HSE Faculty of Computer Science and Yandex are holding the Olympiad for the fourth time. This year's Platinum Partner is ‘Otkritie’ Bank. The Olympiad is organised by leading data analysts for their future colleagues, early career analysts and scientists.

The online tour will focus on the search for dark matter - one of the few remaining mysteries of fundamental physics. Dark matter cannot be seen because it does not interact with light and interacts very weakly with ordinary matter. The task of IDAO participants is to build a model that recognises some known observation processes, so that they can be excluded from the search for dark matter.

Details and registration https://idao.world