U-Net Training with Instance-Layer Normalization
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Authors: Xiao-Yun Zhou, Qing-Biao Li, Mali Shen, Peichao Li, Zhao-Yang Wang, Guang-Zhong Yang
Abstract: Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed
https://arxiv.org/abs/1908.08466
🔗 U-Net Training with Instance-Layer Normalization
Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed. Batch-Instance Normalization (BIN) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers. However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN. In this paper, an Instance-Layer Normalization (ILN) layer is proposed by using the Sigmoid function for the feature map combination, and cascading group normalization. The performance of ILN is validated on image segmentation of the Right Ventricle (RV) and Left Ventricle (LV) using U-Net
Наш телеграм канал - tglink.me/ai_machinelearning_big_data
Authors: Xiao-Yun Zhou, Qing-Biao Li, Mali Shen, Peichao Li, Zhao-Yang Wang, Guang-Zhong Yang
Abstract: Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed
https://arxiv.org/abs/1908.08466
🔗 U-Net Training with Instance-Layer Normalization
Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most of existing methods, the normalization for each layer is fixed. Batch-Instance Normalization (BIN) is one of the first proposed methods that combines two different normalization methods and achieve diverse normalization for different layers. However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN. In this paper, an Instance-Layer Normalization (ILN) layer is proposed by using the Sigmoid function for the feature map combination, and cascading group normalization. The performance of ILN is validated on image segmentation of the Right Ventricle (RV) and Left Ventricle (LV) using U-Net
The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python
https://towardsdatascience.com/the-poisson-process-everything-you-need-to-know-322aa0ab9e9a?source=collection_home---4------2-----------------------
🔗 The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python
Learn about the Poisson process and how to simulate it using Python
https://towardsdatascience.com/the-poisson-process-everything-you-need-to-know-322aa0ab9e9a?source=collection_home---4------2-----------------------
🔗 The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python
Medium
The Poisson Process: Everything you need to know
Learn about the Poisson process and how to simulate it using Python
What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it
https://towardsdatascience.com/what-is-the-difference-between-optimization-and-deep-learning-and-why-should-you-care-e4dc7c2494fe?source=collection_home---4------0-----------------------
🔗 What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it
Deep Learning is not just Optimization and we need to do something about it
https://towardsdatascience.com/what-is-the-difference-between-optimization-and-deep-learning-and-why-should-you-care-e4dc7c2494fe?source=collection_home---4------0-----------------------
🔗 What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it
Medium
What is the difference between Optimization and Deep Learning and why should you care
Deep Learning is not just Optimization and we need to do something about it
A New Consciousness of Inclusion in Machine Learning
http://blog.shakirm.com/2019/06/a-new-consciousness-of-inclusion-in-machine-learning/
🔗 A New Consciousness of Inclusion in Machine Learning
On LGBT Freedoms and our Support for Machine Learning in Africa This is an exploration of my thinking and my personal views. Read in · 1147 words · Soon, in two neighbouring countries in Africa, tw…
http://blog.shakirm.com/2019/06/a-new-consciousness-of-inclusion-in-machine-learning/
🔗 A New Consciousness of Inclusion in Machine Learning
On LGBT Freedoms and our Support for Machine Learning in Africa This is an exploration of my thinking and my personal views. Read in · 1147 words · Soon, in two neighbouring countries in Africa, tw…
The Spectator
A New Consciousness of Inclusion in Machine Learning
On LGBT Freedoms and our Support for Machine Learning in Africa This is an exploration of my thinking and my personal views. Read in · 1147 words · Soon, in two neighbouring countries in Africa, tw…
🎥 Industrialized Capsule Net for Text Analytics by Dr. Vijay Agneeswaran & Abhishek Kumar #ODSC_India
👁 1 раз ⏳ 2683 сек.
👁 1 раз ⏳ 2683 сек.
Multi-label text classification is an interesting problem where multiple tags or categories may have to be associated with the given text/documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (gene classification problem, see [Zafer Barutcuoglu et. al 2006]). Kaggle data set is representative of the problem: https://www.kaggle.com/jhoward/nb-svm-strong-linear-baseline/data.
Several other interesting problem in text anaVk
Industrialized Capsule Net for Text Analytics by Dr. Vijay Agneeswaran & Abhishek Kumar #ODSC_India
Multi-label text classification is an interesting problem where multiple tags or categories may have to be associated with the given text/documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization…
Машинное обучение
#video
🎥 Лекция 1 | Машинное обучение | Сергей Николенко | Лекториум
👁 2 раз ⏳ 5396 сек.
🎥 Лекция 2 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 4251 сек.
🎥 Лекция 3 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 3352 сек.
🎥 Лекция 4 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 6109 сек.
🎥 Лекция 5 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 5170 сек.
🎥 Лекция 6 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 5297 сек.
#video
🎥 Лекция 1 | Машинное обучение | Сергей Николенко | Лекториум
👁 2 раз ⏳ 5396 сек.
Лекция 1 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...🎥 Лекция 2 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 4251 сек.
Лекция 2 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...🎥 Лекция 3 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 3352 сек.
Лекция 3 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...🎥 Лекция 4 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 6109 сек.
Лекция 4 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...🎥 Лекция 5 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 5170 сек.
Лекция 5 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...🎥 Лекция 6 | Машинное обучение | Сергей Николенко | Лекториум
👁 1 раз ⏳ 5297 сек.
Лекция 6 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ
Смотрите это виде...Vk
Лекция 1 | Машинное обучение | Сергей Николенко | Лекториум
Лекция 1 | Курс: Машинное обучение | Лектор: Сергей Николенко | Организатор: Математическая лаборатория имени П.Л.Чебышева СПбГУ Смотрите это виде...
рименение R для утилитарных задач
#Data Mining
Хороший инструмент + наличие навыков работы с ним, что достигается путем практики, позволяет легко и элегантно решать множество различных «как бы» нетипичных задач. Ниже пара подобных примеров. Уверен, что многие могут этот список расширить.
https://habr.com/ru/post/464849/
🔗 Применение R для утилитарных задач
Хороший инструмент + наличие навыков работы с ним, что достигается путем практики, позволяет легко и элегантно решать множество различных «как бы» нетипичных зад...
#Data Mining
Хороший инструмент + наличие навыков работы с ним, что достигается путем практики, позволяет легко и элегантно решать множество различных «как бы» нетипичных задач. Ниже пара подобных примеров. Уверен, что многие могут этот список расширить.
https://habr.com/ru/post/464849/
🔗 Применение R для утилитарных задач
Хороший инструмент + наличие навыков работы с ним, что достигается путем практики, позволяет легко и элегантно решать множество различных «как бы» нетипичных зад...
Хабр
Применение R для утилитарных задач
Хороший инструмент + наличие навыков работы с ним, что достигается путем практики, позволяет легко и элегантно решать множество различных «как бы» нетипичных задач. Ниже пара подобных примеров....
Visualizing Eigenvalues and Eigenvectors
🔗 Visualizing Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors are a very important concept in Linear Algebra and Machine Learning in general. In my previous article, I’ve…
🔗 Visualizing Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors are a very important concept in Linear Algebra and Machine Learning in general. In my previous article, I’ve…
Medium
Visualizing Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors are a very important concept in Linear Algebra and Machine Learning in general. In my previous article, I’ve…
15 книг по машинному обучению для начинающих
Сделал подборку книг по Machine Learning для тех, кто хочет разобраться, что да как.
Добавляйте в закладки и делитесь с коллегами!
https://habr.com/ru/post/464871/
🔗 15 книг по машинному обучению для начинающих
Сделал подборку книг по Machine Learning для тех, кто хочет разобраться, что да как. Добавляйте в закладки и делитесь с коллегами! Книги по машинному обучению...
Сделал подборку книг по Machine Learning для тех, кто хочет разобраться, что да как.
Добавляйте в закладки и делитесь с коллегами!
https://habr.com/ru/post/464871/
🔗 15 книг по машинному обучению для начинающих
Сделал подборку книг по Machine Learning для тех, кто хочет разобраться, что да как. Добавляйте в закладки и делитесь с коллегами! Книги по машинному обучению...
Хабр
15 книг по машинному обучению для начинающих
Сделал подборку книг по Machine Learning для тех, кто хочет разобраться, что да как. Добавляйте в закладки и делитесь с коллегами! Книги по машинному обучению на русском 1. «Математические основы...
Eric Weinstein: Struggle Mightily but Give Yourself a Break | AI Podcast Clips
🔗 Eric Weinstein: Struggle Mightily but Give Yourself a Break | AI Podcast Clips
This is a clip from a conversation with Eric Weinstein on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2Hp8due If you enjoy these, consider subscribing, sharing, and commenting below. Full episode: http://bit.ly/2Hp8due Full episodes playlist: http://bit.ly/2EcbaKf Clips playlist: http://bit.ly/2JYkbfZ Podcast website: https://lexfridman.com/ai Eric Weinstein is a mathematician, economist, physicist, and managing director of Thiel Capital. He formed the "int
🔗 Eric Weinstein: Struggle Mightily but Give Yourself a Break | AI Podcast Clips
This is a clip from a conversation with Eric Weinstein on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2Hp8due If you enjoy these, consider subscribing, sharing, and commenting below. Full episode: http://bit.ly/2Hp8due Full episodes playlist: http://bit.ly/2EcbaKf Clips playlist: http://bit.ly/2JYkbfZ Podcast website: https://lexfridman.com/ai Eric Weinstein is a mathematician, economist, physicist, and managing director of Thiel Capital. He formed the "int
YouTube
Eric Weinstein: Struggle Mightily but Give Yourself a Break | AI Podcast Clips
This is a clip from a conversation with Eric Weinstein on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2Hp8due If you enjoy these, consider subscribing, sharing, and commenting below.
Full episode: http://bit.ly/2Hp8due…
Full episode: http://bit.ly/2Hp8due…
Sparkify user churn prediction using PySpark
Predicting music streaming service user churn on local machine and AWS EMR.
https://medium.com/@jiewwantan/sparkify-user-churn-prediction-using-pyspark-32be364e8296?source=topic_page---------6------------------1
🔗 Sparkify user churn prediction using Pyspark
Predicting music streaming service user churn on local machine and AWS EMR.
Predicting music streaming service user churn on local machine and AWS EMR.
https://medium.com/@jiewwantan/sparkify-user-churn-prediction-using-pyspark-32be364e8296?source=topic_page---------6------------------1
🔗 Sparkify user churn prediction using Pyspark
Predicting music streaming service user churn on local machine and AWS EMR.
Medium
Sparkify user churn prediction using Pyspark
Predicting music streaming service user churn on local machine and AWS EMR.
On the convergence of single-call stochastic extra-gradient methods
Authors: Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
Abstract: Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems).
https://arxiv.org/abs/1908.08465
🔗 On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems). In this setting, the optimal $\mathcal{O}(1/t)$ convergence rate for solving smooth monotone variational inequalities is achieved by the Extra-Gradient (EG) algorithm and its variants. Aiming to alleviate the cost of an extra gradient step per iteration (which can become quite substantial in deep learning applications), several algorithms have been proposed as surrogates to Extra-Gradient with a \emph{single} oracle call per iteration. In this paper, we develop a synthetic view of such algorithms, and we complement the existing literature by showing that they retain a $\mathcal{O}(1/t)$ ergodic convergence rate in smooth, deterministic problems. Subsequently, beyond the monotone deterministic case, we also show that the last i
Authors: Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
Abstract: Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems).
https://arxiv.org/abs/1908.08465
🔗 On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems). In this setting, the optimal $\mathcal{O}(1/t)$ convergence rate for solving smooth monotone variational inequalities is achieved by the Extra-Gradient (EG) algorithm and its variants. Aiming to alleviate the cost of an extra gradient step per iteration (which can become quite substantial in deep learning applications), several algorithms have been proposed as surrogates to Extra-Gradient with a \emph{single} oracle call per iteration. In this paper, we develop a synthetic view of such algorithms, and we complement the existing literature by showing that they retain a $\mathcal{O}(1/t)$ ergodic convergence rate in smooth, deterministic problems. Subsequently, beyond the monotone deterministic case, we also show that the last i
A Gentle Introduction to BigGAN the Big Generative Adversarial Network
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results.
https://machinelearningmastery.com/a-gentle-introduction-to-the-biggan/
🔗 A Gentle Introduction to BigGAN the Big Generative Adversarial Network
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best …
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results.
https://machinelearningmastery.com/a-gentle-introduction-to-the-biggan/
🔗 A Gentle Introduction to BigGAN the Big Generative Adversarial Network
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best …
MachineLearningMastery.com
A Gentle Introduction to BigGAN the Big Generative Adversarial Network - MachineLearningMastery.com
Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis.
Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations…
Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations…
OpenPose, PNASNet 5 for Pose Classification Competition (Fastai)
I recently competed in a local AI competition where the challenge involved human pose classification with 15 different classes.
https://towardsdatascience.com/openpose-pnasnet-5-for-pose-classification-competition-fastai-dc35709158d0?source=collection_home---4------2-----------------------
🔗 OpenPose, PNASNet 5 for Pose Classification Competition (Fastai)
I recently competed in a local AI competition where the challenge involved human pose classification with 15 different classes.
I recently competed in a local AI competition where the challenge involved human pose classification with 15 different classes.
https://towardsdatascience.com/openpose-pnasnet-5-for-pose-classification-competition-fastai-dc35709158d0?source=collection_home---4------2-----------------------
🔗 OpenPose, PNASNet 5 for Pose Classification Competition (Fastai)
I recently competed in a local AI competition where the challenge involved human pose classification with 15 different classes.
Medium
OpenPose, PNASNet 5 for Pose Classification Competition (Fastai)
I recently competed in a local AI competition where the challenge involved human pose classification with 15 different classes.
Transcribe Live Chess with Machine Learning Part 1
In Part 1 we will create a synthetic dataset in Unity and train a model in Keras to transcribe images of chess positions.
https://towardsdatascience.com/transcribe-live-chess-with-machine-learning-part-1-928f73306e1f?source=collection_home---4------0-----------------------
🔗 Transcribe Live Chess with Machine Learning Part 1
In Part 1 we will create a synthetic dataset in Unity and train a model in Keras to transcribe images of chess positions.
In Part 1 we will create a synthetic dataset in Unity and train a model in Keras to transcribe images of chess positions.
https://towardsdatascience.com/transcribe-live-chess-with-machine-learning-part-1-928f73306e1f?source=collection_home---4------0-----------------------
🔗 Transcribe Live Chess with Machine Learning Part 1
In Part 1 we will create a synthetic dataset in Unity and train a model in Keras to transcribe images of chess positions.
Medium
Transcribe Live Chess with Machine Learning Part 1
In Part 1 we will create a synthetic dataset in Unity and train a model in Keras to transcribe images of chess positions.
+100 AI Cheatsheets
List of Free AI Courses
https://github.com/Niraj-Lunavat/Artificial-Intelligen
🔗 Niraj-Lunavat/Artificial-Intelligence
Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks...
List of Free AI Courses
https://github.com/Niraj-Lunavat/Artificial-Intelligen
🔗 Niraj-Lunavat/Artificial-Intelligence
Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks...
Линейная алгебра
00 - Линейная алгебра. О курсе
01 - Линейная алгебра. Линейное (векторное) пространство
02 - Линейная алгебра. Существование решений систем линейных уравнений
03 - Линейная алгебра. Решение систем линейных алгебраических уравнений
04 - Линейная алгебра. Евклидово пространство
05 - Линейная алгебра. Ортогональный базис
06 - Линейная алгебра. Линейные операторы
07 - Линейная алгебра. Определитель и ориентированный объем
08 - Линейная алгебра. Свойства определителя (часть 1)
09 - Линейная алгебра. Свойства определителя (часть 2)
🎥 00 - Линейная алгебра. О курсе
👁 1668 раз ⏳ 69 сек.
🎥 01 - Линейная алгебра. Линейное (векторное) пространство
👁 148 раз ⏳ 1316 сек.
🎥 02 - Линейная алгебра. Существование решений систем линейных уравнений
👁 95 раз ⏳ 1512 сек.
🎥 03 - Линейная алгебра. Решение систем линейных алгебраических уравнений
👁 35 раз ⏳ 2116 сек.
🎥 04 - Линейная алгебра. Евклидово пространство
👁 24 раз ⏳ 1480 сек.
🎥 05 - Линейная алгебра. Ортогональный базис
👁 17 раз ⏳ 2370 сек.
🎥 06 - Линейная алгебра. Линейные операторы
👁 16 раз ⏳ 1948 сек.
🎥 07 - Линейная алгебра. Определитель и ориентированный объем
👁 10 раз ⏳ 2207 сек.
🎥 08 - Линейная алгебра. Свойства определителя (часть 1)
👁 13 раз ⏳ 2671 сек.
🎥 09 - Линейная алгебра. Свойства определителя (часть 2)
👁 44 раз ⏳ 2201 сек.
00 - Линейная алгебра. О курсе
01 - Линейная алгебра. Линейное (векторное) пространство
02 - Линейная алгебра. Существование решений систем линейных уравнений
03 - Линейная алгебра. Решение систем линейных алгебраических уравнений
04 - Линейная алгебра. Евклидово пространство
05 - Линейная алгебра. Ортогональный базис
06 - Линейная алгебра. Линейные операторы
07 - Линейная алгебра. Определитель и ориентированный объем
08 - Линейная алгебра. Свойства определителя (часть 1)
09 - Линейная алгебра. Свойства определителя (часть 2)
🎥 00 - Линейная алгебра. О курсе
👁 1668 раз ⏳ 69 сек.
🎥 01 - Линейная алгебра. Линейное (векторное) пространство
👁 148 раз ⏳ 1316 сек.
🎥 02 - Линейная алгебра. Существование решений систем линейных уравнений
👁 95 раз ⏳ 1512 сек.
🎥 03 - Линейная алгебра. Решение систем линейных алгебраических уравнений
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