VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Github: https://github.com/deeplearning-wisc/vos

Paper: https://arxiv.org/pdf/2202.01197v2.pdf

Dataset: https://paperswithcode.com/dataset/bdd100k

@ai_machinelearning_big_data

🦾 The easiest way to the neuroscience world with the shield for RaspberryPi

Github: https://github.com/Ildaron/EEGwithRaspberryPI

Paper: https://arxiv.org/pdf/2202.01936v1.pdf

Project: https://www.crowdsupply.com/hackerbci/pieeg

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🖌 Two-Dimensional Tensors in Pytorch

https://machinelearningmastery.com/two-dimensional-tensors-in-pytorch/

One-Dimensional Tensors: https://machinelearningmastery.com/one-dimensional-tensors-in-pytorch/

PyTorch tensor Tutorial: https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html

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🗒 DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

Github: https://github.com/j-min/dalleval

Paper: https://arxiv.org/pdf/2202.01936v1.pdf

Data: https://drive.google.com/drive/folders/1Bza2zyvHLvComohZ9PAGyykY7sm7JoIH

Dataset: https://paperswithcode.com/dataset/conceptual-captions

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https://news.1rj.ru/str/Machinelearningtest - machine learning tests

🎬 FILM: Frame Interpolation for Large Scene Motion

Github: https://github.com/google-research/frame-interpolation

Paper: https://arxiv.org/pdf/2202.04901.pdf

Video: https://www.youtube.com/watch?v=OAD-BieIjH4

Project: https://film-net.github.io/

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Опубликованы новые материалы по Machine Learning от Школы анализа данных Яндекса 🔥

То самое пособие, которое ШАД разместила в открытом доступе, пополнилось новым разделом — про базовые архитектуры и обучение нейросетей. Чтобы вы лучше разобрались в предыдущих темах, авторы также добавили главы о математике ML: матричное дифференцирование и bias-variance decomposition.

Возьмитесь за основательное изучение Machine Learning и сохраняйте ссылку на онлайн-учебник: https://clck.ru/b33aZ

P.S. Пособие регулярно обновляется, и в скором времени в нём появятся материалы о вероятностном подходе к ML и решении сложных задач Data Science. Следите за выходом новых глав!

💡 A lightweight vision library for performing large scale object detection & instance segmentation

Github: https://github.com/obss/sahi

Paper: https://arxiv.org/abs/2202.06934v1

Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx

Dataset: https://paperswithcode.com/dataset/xview


@ai_machinelearning_big_data

🐙 OCTIS : Optimizing and Comparing Topic Models is Simple!

Github: https://github.com/mind-Lab/octis

Paper: https://arxiv.org/abs/2202.07631v1

Dataset: https://paperswithcode.com/dataset/20-newsgroups

@ai_machinelearning_big_data

🗣 Facebbok’s textless-lib: a Library for Textless Spoken Language Processing

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

Code examples: https://github.com/facebookresearch/textlesslib/tree/main/examples

Paper: https://arxiv.org/abs/2202.07359v1

Dataset: https://paperswithcode.com/dataset/librispeech

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🔎 Anomalib: A Deep Learning Library for Anomaly Detection

Github: https://github.com/openvinotoolkit/anomalib

Docs: https://openvinotoolkit.github.io/anomalib/

Paper: https://arxiv.org/abs/2202.08341v1

Dataset: https://paperswithcode.com/dataset/btad

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💻 Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

Github: https://github.com/nvlabs/instant-ngp

HashNeRF-pytorch: https://openvinotoolkit.github.io/anomalib/

Paper: https://arxiv.org/abs/2201.05989v1


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Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation

Github: https://github.com/tzer-anonbot/tzer

Docs: https://tzer.readthedocs.io/en/latest/markdown/artifact.html

Paper: https://arxiv.org/abs/2202.09947v1


@ai_machinelearning_big_data

🔥 The Complete Collection of Data Science Cheat Sheets

https://www.kdnuggets.com/2022/02/complete-collection-data-science-cheat-sheets-part-2.html

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☑️ One-shot Affordance Detection

Github: https://github.com/lhc1224/OSAD_Net

Paper: https://arxiv.org/abs/2202.12076v1

Dataset: https://paperswithcode.com/dataset/pad

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👁 Visual Attention Network (VAN)

Github: https://github.com/Visual-Attention-Network/VAN-Classification

Paper: https://arxiv.org/pdf/2202.09741.pdf

Dataset: https://paperswithcode.com/dataset/ade20k

@ai_machinelearning_big_data

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💥 Local and Global GANs with Semantic-Aware Upsampling for Image Generation

Github: https://github.com/Ha0Tang/LGGAN

Paper: https://arxiv.org/abs/2203.00047v1

Dataset: https://paperswithcode.com/dataset/cityscapes

@ai_machinelearning_big_data

🎲 Bayesian IRT models in Python

Github: https://github.com/nd-ball/py-irt

Paper: https://arxiv.org/abs/2203.01282v1

Bayesian IRT: https://m-clark.github.io/models-by-example/bayesian-irt.html

@ai_machinelearning_big_data

Hello everyone. My name is Andrew and for several years I've been working on to make the learning path for ML
easier.

I wrote a manual on machine learning that
everyone understands - Machine Learning Simplified Book. The main purpose of my book is to build an intuitive
understanding of how algorithms work through basic examples. In order to understand the presented material,
it is enough to know basic mathematics and linear algebra.

After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical
professionals.


You can read the book absolutely free at the link below:
-> https://themlsbook.com

⚫️ Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

Github: https://github.com/ghostish/open3dsot

Paper: https://arxiv.org/abs/2203.01730v1

Dataset: https://paperswithcode.com/dataset/kitti

@ai_machinelearning_big_data