Progressive #VAE Training on Highly Sparse and Imbalanced Data
In this paper, we present a novel approach for training a #VariationalAutoencoder (VAE) on a highly imbalanced data set. The proposed training of a high-resolution VAE model begins with the training of a low-resolution core model, which can be successfully trained on imbalanced data set. In subsequent training steps, new convolutional, upsampling, deconvolutional, and downsampling layers are iteratively attached to the model. In each iteration, the additional layers are trained based on the intermediate pretrained model - a result of previous training iterations. Thus, the resolution of the model is progressively increased up to the required resolution level. In this paper, the progressive VAE training is exploited for learning a latent representation with imbalanced, highly sparse data sets and, consequently, generating routes in a constrained 2D space. Routing problems (e.g., vehicle routing problem, travelling salesman problem, and arc routing) are of special significance in many modern applications (e.g., route planning, network maintenance, developing high-performance nanoelectronic systems, and others) and typically associated with sparse imbalanced data. In this paper, the critical problem of routing billions of components in nanoelectronic devices is considered. The proposed approach exhibits a significant training speedup as compared with state-of-the-art existing VAE training methods, while generating expected image outputs from unseen input data. Furthermore, the final progressive VAE models exhibit much more precise output representation, than the #GenerativeAdversarialNetwork ( #GAN ) models trained with comparable training time. The proposed method is expected to be applicable to a wide range of applications, including but not limited image impainting, sentence interpolation, and semi-supervised learning.
Paper
🔭 @DeepGravity
In this paper, we present a novel approach for training a #VariationalAutoencoder (VAE) on a highly imbalanced data set. The proposed training of a high-resolution VAE model begins with the training of a low-resolution core model, which can be successfully trained on imbalanced data set. In subsequent training steps, new convolutional, upsampling, deconvolutional, and downsampling layers are iteratively attached to the model. In each iteration, the additional layers are trained based on the intermediate pretrained model - a result of previous training iterations. Thus, the resolution of the model is progressively increased up to the required resolution level. In this paper, the progressive VAE training is exploited for learning a latent representation with imbalanced, highly sparse data sets and, consequently, generating routes in a constrained 2D space. Routing problems (e.g., vehicle routing problem, travelling salesman problem, and arc routing) are of special significance in many modern applications (e.g., route planning, network maintenance, developing high-performance nanoelectronic systems, and others) and typically associated with sparse imbalanced data. In this paper, the critical problem of routing billions of components in nanoelectronic devices is considered. The proposed approach exhibits a significant training speedup as compared with state-of-the-art existing VAE training methods, while generating expected image outputs from unseen input data. Furthermore, the final progressive VAE models exhibit much more precise output representation, than the #GenerativeAdversarialNetwork ( #GAN ) models trained with comparable training time. The proposed method is expected to be applicable to a wide range of applications, including but not limited image impainting, sentence interpolation, and semi-supervised learning.
Paper
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This project is real-time visualization of a network recognizing digits from user's input.
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GitHub
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YouTube
GitHub
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YouTube
Neural network visualization
This is real-time visualization of a network recognizing digits from user's input. I used Processing to implement this. You can check my Github if you want t...
Image Data Augmentation for #TensorFlow 2, #Keras and #PyTorch with Albumentations in #Python
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
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TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
Curiousily
Image Data Augmentation for TensorFlow 2, Keras and PyTorch with Albumentations in Python - Adventures in Artificial Intelligence…
Learn how to augment image data for Image Classification, Object Detection, and Image Segmentation
A Gentle Introduction to Imbalanced #Classification
After completing this tutorial, you will know:
* Imbalanced classification is the problem of classification when there is an unequal distribution of classes in the training dataset.
* The imbalance in the class distribution may vary, but a severe imbalance is more challenging to model and may require specialized techniques.
* Many real-world classification problems have an imbalanced class distribution, such as fraud detection, spam detection, and churn prediction.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
* Imbalanced classification is the problem of classification when there is an unequal distribution of classes in the training dataset.
* The imbalance in the class distribution may vary, but a severe imbalance is more challenging to model and may require specialized techniques.
* Many real-world classification problems have an imbalanced class distribution, such as fraud detection, spam detection, and churn prediction.
Link
🔭 @DeepGravity
Machine Learning Mastery
A Gentle Introduction to Imbalanced Classification
Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The…
Deep Gravity pinned «Our reinforcement learning architect designs have been just published on #NeurIPS2019 AI Art Gallery: https://lnkd.in/dFZ37BN Seems it draws like a baby now, but is growing and hopefully would be a skillful #RL artist very soon. #reinforcementlearning…»
5 #Financial Services Tech Trends to Watch in 2020
1. The Role of #ArtificialIntelligence in Finance Will Expand
2. Financial Services Firms Will Grow Their Use of #Data Analytics
3. #Blockchain Will Be a Key Security Solution
4. More #Bank Branches Will Undergo Digital Transformations
5. Automation Will Take Over More Financial Services
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1. The Role of #ArtificialIntelligence in Finance Will Expand
2. Financial Services Firms Will Grow Their Use of #Data Analytics
3. #Blockchain Will Be a Key Security Solution
4. More #Bank Branches Will Undergo Digital Transformations
5. Automation Will Take Over More Financial Services
Link
🔭 @DeepGravity
Technology Solutions That Drive Business
5 Financial Services Tech Trends to Watch in 2020
AI, blockchain and automation are among the trends poised to alter the financial services industry.
Inside #DeepMind 's epic mission to solve science's trickiest problem
DeepMind's AI has beaten chess grandmasters and #Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives
Link
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DeepMind's AI has beaten chess grandmasters and #Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives
Link
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WIRED
Inside DeepMind's epic mission to solve science's trickiest problem
DeepMind's AI has beaten chess grandmasters and Go champions. But founder and CEO Demis Hassabis now has his sights set on bigger, real-world problems that could change lives. First up: protein folding
Explaining #ReinforcementLearning: Active vs Passive
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Article
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We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Article
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KDnuggets
Explaining Reinforcement Learning: Active vs Passive - KDnuggets
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Prediction of Physical Load Level by #MachineLearning Analysis of Heart Activity after Exercises
Paper
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Paper
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Triple #GenerativeAdversarialNetworks
Generative adversarial networks (GANs) have shown promise in image generation and classification given limited supervision. Existing methods extend the unsupervised GAN framework to incorporate supervision heuristically. Specifically, a single discriminator plays two incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. The formulation intrinsically causes two problems: (1) the generator and the discriminator (i.e., the classifier) may not converge to the data distribution at the same time; and (2) the generator cannot control the semantics of the generated samples. In this paper, we present the triple generative adversarial network (Triple-GAN), which consists of three players—a generator, a classifier, and a discriminator. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible objective functions to ensure that the distributions characterized by the generator and the classifier converge to the data distribution. We evaluate Triple-GAN in two challenging settings, namely, semi-supervised learning and the extreme low data regime. In both settings, Triple-GAN can achieve state-of-the-art classification results among deep generative models and generate meaningful samples in a specific class simultaneously.
Paper
🔭 @DeepGravity
Generative adversarial networks (GANs) have shown promise in image generation and classification given limited supervision. Existing methods extend the unsupervised GAN framework to incorporate supervision heuristically. Specifically, a single discriminator plays two incompatible roles of identifying fake samples and predicting labels and it only estimates the data without considering the labels. The formulation intrinsically causes two problems: (1) the generator and the discriminator (i.e., the classifier) may not converge to the data distribution at the same time; and (2) the generator cannot control the semantics of the generated samples. In this paper, we present the triple generative adversarial network (Triple-GAN), which consists of three players—a generator, a classifier, and a discriminator. The generator and the classifier characterize the conditional distributions between images and labels, and the discriminator solely focuses on identifying fake image-label pairs. We design compatible objective functions to ensure that the distributions characterized by the generator and the classifier converge to the data distribution. We evaluate Triple-GAN in two challenging settings, namely, semi-supervised learning and the extreme low data regime. In both settings, Triple-GAN can achieve state-of-the-art classification results among deep generative models and generate meaningful samples in a specific class simultaneously.
Paper
🔭 @DeepGravity
Hyperparameter Tuning On #Google Cloud Platform With #Scikit_Learn
Google Cloud Platform’s AI Platform (formerly ML Engine) offers a hyperparameter tuning service for your models. Why should you take the extra time and effort to learn how to use it instead of just running the code you already have on a virtual machine? Are the benefits worth the extra time and effort?
Link
🔭 @DeepGravity
Google Cloud Platform’s AI Platform (formerly ML Engine) offers a hyperparameter tuning service for your models. Why should you take the extra time and effort to learn how to use it instead of just running the code you already have on a virtual machine? Are the benefits worth the extra time and effort?
Link
🔭 @DeepGravity
Medium
Hyperparameter Tuning On Google Cloud Platform With Scikit-Learn
Google Cloud Platform’s AI Platform (formerly ML Engine) offers a hyperparameter tuning service for your models. Why should you take the…
Neural #Quantum States
How #neuralnetworks can solve highly complex problems in quantum mechanics
Article
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How #neuralnetworks can solve highly complex problems in quantum mechanics
Article
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Medium
Neural Quantum States
How neural networks can solve highly complex problems in quantum mechanics
Automating Pac-man with #DeepQLearning: An Implementation in #Tensorflow.
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#DeepReinforcementLearning
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#DeepReinforcementLearning
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Medium
Automating Pac-man with Deep Q-learning: An Implementation in Tensorflow.
Fundamentals of Reinforcement Learning