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🚘 Highway-env
A collection of environments for autonomous driving and tactical decision-making tasks
Github: https://github.com/eleurent/highway-env
Documentation: https://highway-env.readthedocs.io/en/latest/
Paper: https://arxiv.org/abs/2105.05701v1
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A collection of environments for autonomous driving and tactical decision-making tasks
Github: https://github.com/eleurent/highway-env
Documentation: https://highway-env.readthedocs.io/en/latest/
Paper: https://arxiv.org/abs/2105.05701v1
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Forwarded from Машинное обучение RU
Многослойная нормализация: новый метод улучшения эффективности нейронных сетей
https://neurohive.io/ru/novosti/mnogoslojnaya-normalizaciya-novyj-metod-uluchsheniya-effektivnosti-nejronnyh-setej/
En: https://www.frontiersin.org/articles/10.3389/fnins.2021.626277/full
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https://neurohive.io/ru/novosti/mnogoslojnaya-normalizaciya-novyj-metod-uluchsheniya-effektivnosti-nejronnyh-setej/
En: https://www.frontiersin.org/articles/10.3389/fnins.2021.626277/full
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🧠 Teaching AI how to forget at scale
Video: https://www.youtube.com/watch?v=hI6iJmPgm_k&ab_channel=FacebookAIFacebookAI
Facebook AI: https://ai.facebook.com/blog/teaching-ai-how-to-forget-at-scale/
Github: https://github.com/facebookresearch/transformer-sequential
Paper: https://ai.facebook.com/research/publications/not-all-memories-are-created-equal
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Video: https://www.youtube.com/watch?v=hI6iJmPgm_k&ab_channel=FacebookAIFacebookAI
Facebook AI: https://ai.facebook.com/blog/teaching-ai-how-to-forget-at-scale/
Github: https://github.com/facebookresearch/transformer-sequential
Paper: https://ai.facebook.com/research/publications/not-all-memories-are-created-equal
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YouTube
Expire-Span: Teaching AI How to Forget at Scale
As a step toward achieving humanlike memory in machines, we’re announcing Expire-Span, a first-of-its-kind method that equips neural networks with the ability to forget at scale. Learn more on the blog: https://ai.facebook.com/blog/teaching-ai-how-to-forget…
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📷 NeRF Meta Learning With PyTorch
Given a single input view, meta-initialized NeRF can generate a 360-degree video.
Github: https://github.com/sanowar-raihan/nerf-meta
Paper: https://arxiv.org/abs/2012.02189
Original Project Page: https://www.matthewtancik.com/learnit
Official JAX Implementation: https://github.com/tancik/learnit
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Given a single input view, meta-initialized NeRF can generate a 360-degree video.
Github: https://github.com/sanowar-raihan/nerf-meta
Paper: https://arxiv.org/abs/2012.02189
Original Project Page: https://www.matthewtancik.com/learnit
Official JAX Implementation: https://github.com/tancik/learnit
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💥DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
Github: https://nv-tlabs.github.io/datasetGAN/
Article: https://www.infoq.com/news/2021/05/nvidia-dataset-generator/
Ru: https://neurohive.io/ru/novosti/datasetgan-generator-sinteticheskih-annotirovannyh-datasetov-nvidia/
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Github: https://nv-tlabs.github.io/datasetGAN/
Article: https://www.infoq.com/news/2021/05/nvidia-dataset-generator/
Ru: https://neurohive.io/ru/novosti/datasetgan-generator-sinteticheskih-annotirovannyh-datasetov-nvidia/
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📕 Font Style that Fits an Image -- Font Generation Based on Image Context
Github: https://github.com/Taylister/FontFits
Paper: https://arxiv.org/abs/2105.08879v1
Dataset creation: https://github.com/Taylister/TGNet-Datagen
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Github: https://github.com/Taylister/FontFits
Paper: https://arxiv.org/abs/2105.08879v1
Dataset creation: https://github.com/Taylister/TGNet-Datagen
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🔥 Awesome list of datasets in 100+ categories
44 zettabytes of data
https://www.kdnuggets.com/2021/05/awesome-list-datasets.html
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44 zettabytes of data
https://www.kdnuggets.com/2021/05/awesome-list-datasets.html
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🎉 24 мая в Москве наградили молодых ученых и их наставников, занимающихся научной работой в области компьютерных наук. Поздравляем лауреатов премии имени Ильи Сегаловича! 🎉
Каждый из них получит по 1 миллиону рублей, который будет можно потратить на собственные исследования. В этом году Совет премии отметил шесть исследователей из НИУ ВШЭ, МФТИ и Сколковского института науки и технологий.
⚠️ Узнайте больше о тех, кто получил премию, и как принять в ней участие: https://clck.ru/V3QDF
Каждый из них получит по 1 миллиону рублей, который будет можно потратить на собственные исследования. В этом году Совет премии отметил шесть исследователей из НИУ ВШЭ, МФТИ и Сколковского института науки и технологий.
⚠️ Узнайте больше о тех, кто получил премию, и как принять в ней участие: https://clck.ru/V3QDF
Yandex ML Prize
Премия Яндекса для учёных и преподавателей в области Machine Learning
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🧠 NeuroKit2: A Python toolbox for neurophysiological signal processing
A user-friendly package providing easy access to advanced biosignal processing routines.
Github: https://github.com/neuropsychology/NeuroKit
Paper: https://link.springer.com/article/10.3758/s13428-020-01516-y
Docs: https://neurokit2.readthedocs.io/en/latest/installation.html
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A user-friendly package providing easy access to advanced biosignal processing routines.
Github: https://github.com/neuropsychology/NeuroKit
Paper: https://link.springer.com/article/10.3758/s13428-020-01516-y
Docs: https://neurokit2.readthedocs.io/en/latest/installation.html
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✔️ GPBoost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models
Github: https://github.com/fabsig/GPBoost
Demo code: https://htmlpreview.github.io/?https://github.com/fabsig/GPBoost/blob/master/examples/GPBoost_demo.html
Paper: https://arxiv.org/abs/2105.08966v2
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Github: https://github.com/fabsig/GPBoost
Demo code: https://htmlpreview.github.io/?https://github.com/fabsig/GPBoost/blob/master/examples/GPBoost_demo.html
Paper: https://arxiv.org/abs/2105.08966v2
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🗯 Unsupervised Speech Recognition
Github: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec/unsupervised
Pretraned model: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
Facebook blog: https://ai.facebook.com/blog/wav2vec-unsupervised-speech-recognition-without-supervision/
Paper
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Github: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec/unsupervised
Pretraned model: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
Facebook blog: https://ai.facebook.com/blog/wav2vec-unsupervised-speech-recognition-without-supervision/
Paper
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Yolov5-face is a real-time,high accuracy face detection
Github: https://github.com/deepcam-cn/yolov5-face
Paper: https://arxiv.org/abs/2105.12931v1
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Github: https://github.com/deepcam-cn/yolov5-face
Paper: https://arxiv.org/abs/2105.12931v1
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💥Grokking Artificial Intelligence Algorithms
⬇️ Download
💥Grokking Deep Reinforcement Learning
⬇️ Download
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⬇️ Download
💥Grokking Deep Reinforcement Learning
⬇️ Download
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🏎 Make Pandas 3 Times Faster with PyPolars
Code : https://www.kdnuggets.com/2021/05/pandas-faster-pypolars.html
Github: https://github.com/pola-rs/polars
User Guide: https://pola-rs.github.io/polars-book
@ai_machinelearning_big_data
Code : https://www.kdnuggets.com/2021/05/pandas-faster-pypolars.html
Github: https://github.com/pola-rs/polars
User Guide: https://pola-rs.github.io/polars-book
@ai_machinelearning_big_data
You Only 👀 One Sequence
Rethinking Transformer in Vision through Object Detection
Github: https://paperswithcode.com/paper/you-only-look-at-one-sequence-rethinking
Dataset: https://paperswithcode.com/dataset/imagenet
Paper: https://arxiv.org/abs/2106.00666
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Rethinking Transformer in Vision through Object Detection
Github: https://paperswithcode.com/paper/you-only-look-at-one-sequence-rethinking
Dataset: https://paperswithcode.com/dataset/imagenet
Paper: https://arxiv.org/abs/2106.00666
@ai_machinelearning_big_data
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🌏 The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation
Github: https://github.com/facebookresearch/flores
Paper: https://ai.facebook.com/research/publications/the-flores-101-evaluation-benchmark-for-low-resource-and-multilingual-machine-translation
Facebook blog: https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world/
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Github: https://github.com/facebookresearch/flores
Paper: https://ai.facebook.com/research/publications/the-flores-101-evaluation-benchmark-for-low-resource-and-multilingual-machine-translation
Facebook blog: https://ai.facebook.com/blog/the-flores-101-data-set-helping-build-better-translation-systems-around-the-world/
@ai_machinelearning_big_data
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🤖 DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Project: https://dynamicvit.ivg-research.xyz/
Github: https://github.com/raoyongming/DynamicViT
Paper: https://arxiv.org/abs/2106.02034
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Project: https://dynamicvit.ivg-research.xyz/
Github: https://github.com/raoyongming/DynamicViT
Paper: https://arxiv.org/abs/2106.02034
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X5 Group проводит собственное мероприятие X5Tech Future Night о технологиях и бизнесе. Большое летнее офлайн событие объединит на одной площадке разные форматы: лекции, паблик-интервью, бизнес-дебаты, дискуссии и музыкальный оупен-эйр.
В программе есть отдельная секция, посвященная Big Data, а именно тому, как монетизировать данные и превратить их в новые продукты.
Участие бесплатное, регистрируйтесь сейчас, чтобы не пропустить. Количество мест ограничено!
В программе есть отдельная секция, посвященная Big Data, а именно тому, как монетизировать данные и превратить их в новые продукты.
Участие бесплатное, регистрируйтесь сейчас, чтобы не пропустить. Количество мест ограничено!
📈 NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Github: https://github.com/stanfordmlgroup/ngboost
Slides: https://drive.google.com/file/d/183BWFAdFms81MKy6hSku8qI97OwS_JH_/view
Paper: https://arxiv.org/abs/2106.03823v1
@ai_machinelearning_big_data
Github: https://github.com/stanfordmlgroup/ngboost
Slides: https://drive.google.com/file/d/183BWFAdFms81MKy6hSku8qI97OwS_JH_/view
Paper: https://arxiv.org/abs/2106.03823v1
@ai_machinelearning_big_data
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🧠 Yet Another Language Model — нейросетевой языковой алгоритм генерации текстов, разработанный Яндексом
Paper : https://wow.link/Er21
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Paper : https://wow.link/Er21
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