#VariationalAutoencoder Theory
The Variational Autoencoder has taken the #MachineLearning community by storm since Kingma and Welling’s seminal paper was released in 20131.
Link
🔭 @DeepGravity
The Variational Autoencoder has taken the #MachineLearning community by storm since Kingma and Welling’s seminal paper was released in 20131.
Link
🔭 @DeepGravity
#DecisionTree vs #RandomForest vs #GradientBoostingMachines: Explained Simply
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
Link
🔭 @DeepGravity
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
Link
🔭 @DeepGravity
Semantic Image #Segmentation with #DeepLab in #TensorFlow
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
Link
🔭 @DeepGravity
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
Link
🔭 @DeepGravity
Datasciencecentral
Semantic Image Segmentation with DeepLab in TensorFlow
This article was written by Liang-Chieh Chen and Yukun Zhu.
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
Forwarded from Apply Time Positions
Dr. Mahdi Imani, an assistant professor at the Department of Electrical and Computer Engineering at the George Washington University, seeks for multiple PhD students with interests in Machine Learning, Reinforcement Learning and Statistics. Ideal candidates may have:
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
A very interesting paper by #Harvard University and #OpenAI
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
Openai
Deep double descent
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful…
#Google #DeepMind gamifies memory with its latest #AI work
Google DeepMind scientists built a computer program that gives signals from future to past, in a kind of theoretical model that feels like things people do when they learn from their mistakes. Just remember, it's only a game.
Link
🔭 @DeepGravity
Google DeepMind scientists built a computer program that gives signals from future to past, in a kind of theoretical model that feels like things people do when they learn from their mistakes. Just remember, it's only a game.
Link
🔭 @DeepGravity
ZDNet
Google DeepMind gamifies memory with its latest AI work
Google DeepMind scientists built a computer program that gives signals from future to past, in a kind of theoretical model that feels like things people do when they learn from their mistakes. Just remember, it's only a game.
How #NeuralNetworks work—and why they’ve become a big business
The last decade has seen remarkable improvements in the ability of computers to understand the world around them. Photo software automatically recognizes people's faces. Smartphones transcribe spoken words into text. Self-driving cars recognize objects on the road and avoid hitting them.
Underlying these breakthroughs is an artificial intelligence technique called deep learning. Deep learning is based on neural networks, a type of data structure loosely inspired by networks of biological neurons. Neural networks are organized in layers, with inputs from one layer connected to outputs from the next layer.
Link
🔭 @DeepGravity
The last decade has seen remarkable improvements in the ability of computers to understand the world around them. Photo software automatically recognizes people's faces. Smartphones transcribe spoken words into text. Self-driving cars recognize objects on the road and avoid hitting them.
Underlying these breakthroughs is an artificial intelligence technique called deep learning. Deep learning is based on neural networks, a type of data structure loosely inspired by networks of biological neurons. Neural networks are organized in layers, with inputs from one layer connected to outputs from the next layer.
Link
🔭 @DeepGravity
Ars Technica
How neural networks work—and why they’ve become a big business
Neural networks have grown from an academic curiosity to a massive industry.
#ReinforcementLearning to Reduce Building Energy Consumption
#ModelPredictiveControl (MPC)
The basic #MPC concept can be summarized as follows. Suppose that we wish to control a multiple-input, multiple-output process while satisfying inequality constraints on the input and output variables. If a reasonably accurate dynamic model of the process is available, model and current measurements can be used to predict future values of the outputs. Then the appropriate changes in the input variables can be computed based on both predictions and measurements.
Link
🔭 @DeepGravity
#ModelPredictiveControl (MPC)
The basic #MPC concept can be summarized as follows. Suppose that we wish to control a multiple-input, multiple-output process while satisfying inequality constraints on the input and output variables. If a reasonably accurate dynamic model of the process is available, model and current measurements can be used to predict future values of the outputs. Then the appropriate changes in the input variables can be computed based on both predictions and measurements.
Link
🔭 @DeepGravity
Datasciencecentral
Reinforcement Learning to Reduce Building Energy Consumption
Heating, ventilation, and air conditioning of buildings accounts alone for nearly 40% of the global energy demand [1].
The need for Energy Savings has become i…
The need for Energy Savings has become i…
#StarGAN v2: Diverse Image Synthesis for Multiple Domains
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset can be found at this https URL.
Paper
#GANs
🔭 @DeepGravity
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains. We propose StarGAN v2, a single framework that tackles both and shows significantly improved results over the baselines. Experiments on CelebA-HQ and a new animal faces dataset (AFHQ) validate our superiority in terms of visual quality, diversity, and scalability. To better assess image-to-image translation models, we release AFHQ, high-quality animal faces with large inter- and intra-domain differences. The code, pretrained models, and dataset can be found at this https URL.
Paper
#GANs
🔭 @DeepGravity
A thousand ways to deploy #MachineLearning models
You have done a great work building that awesome 99% accurate machine learning model but your work most of the time is not done without deploying. Most times our models will be integrated with existing web apps, mobile apps or other systems. How then do we make this happen?
Link
🔭 @DeepGravity
You have done a great work building that awesome 99% accurate machine learning model but your work most of the time is not done without deploying. Most times our models will be integrated with existing web apps, mobile apps or other systems. How then do we make this happen?
Link
🔭 @DeepGravity
Medium
A thousand ways to deploy Machine learning models — A.P.I
“What use is a machine learning model if you don’t deploy to production " — Anonymous
#Amazon AWS #DeepComposer is “the world’s first #MachineLearning -enabled musical keyboard”
Link
🔭 @DeepGravity
Link
🔭 @DeepGravity
MusicRadar
Amazon AWS DeepComposer is “the world’s first machine learning-enabled musical keyboard”
Use Generative AI to create a complete track out of a simple melody
How to Use Out-of-Fold Predictions in #MachineLearning
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation.
During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions.
Out-of-fold predictions play an important role in machine learning in both estimating the performance of a model when making predictions on new data in the future, so-called the generalization performance of the model, and in the development of ensemble models.
In this tutorial, you will discover a gentle introduction to out-of-fold predictions in machine learning.
After completing this tutorial, you will know:
*Out-of-fold predictions are a type of out-of-sample predictions made on data not used to train a model.
* Out-of-fold predictions are most commonly used to estimate the performance of a model when making predictions on unseen data.
*Out-of-fold predictions can be used to construct an ensemble model called a stacked generalization or stacking ensemble.
Link
🔭 @DeepGravity
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation.
During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions.
Out-of-fold predictions play an important role in machine learning in both estimating the performance of a model when making predictions on new data in the future, so-called the generalization performance of the model, and in the development of ensemble models.
In this tutorial, you will discover a gentle introduction to out-of-fold predictions in machine learning.
After completing this tutorial, you will know:
*Out-of-fold predictions are a type of out-of-sample predictions made on data not used to train a model.
* Out-of-fold predictions are most commonly used to estimate the performance of a model when making predictions on unseen data.
*Out-of-fold predictions can be used to construct an ensemble model called a stacked generalization or stacking ensemble.
Link
🔭 @DeepGravity
Free #AI #Resources
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
Link
🔭 @DeepGravity
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
Link
🔭 @DeepGravity
MarkTechPost
Free AI/ Data Science Resources
Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.
Understanding #TransferLearning for #Medical Imaging
As #DeepNeuralNetworks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet); and 2) fine-tuning, where the pretrained network is further trained on the specific target task of interest, which may have fewer labeled examples than the pretraining dataset. The pretraining step helps the network learn general features that can be reused on the target task.
Link
#Google
🔭 @DeepGravity
As #DeepNeuralNetworks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet); and 2) fine-tuning, where the pretrained network is further trained on the specific target task of interest, which may have fewer labeled examples than the pretraining dataset. The pretraining step helps the network learn general features that can be reused on the target task.
Link
🔭 @DeepGravity
blog.research.google
Understanding Transfer Learning for Medical Imaging
Simplified Action Decoder for Deep Multi-Agent #ReinforcementLearning
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, #RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.
Paper
🔭 @DeepGravity
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, #RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.
Paper
🔭 @DeepGravity
#MachineLearning and the physical sciences
ABSTRACT
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
Paper
🔭 @DeepGravity
ABSTRACT
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
Paper
🔭 @DeepGravity
How to Implement #Pix2Pix #GAN Models From Scratch With #Keras
What Is the Pix2Pix GAN?
Pix2Pix is a #GenerativeAdversarialNetwork, or GAN, model designed for general purpose image-to-image translation.
The approach was presented by Phillip Isola, et al. in their 2016 paper noscriptd “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.
The GAN architecture is comprised of a generator model for outputting new plausible synthetic images and a discriminator model that classifies images as real (from the dataset) or fake (generated). The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.
Link
🔭 @DeepGravity
What Is the Pix2Pix GAN?
Pix2Pix is a #GenerativeAdversarialNetwork, or GAN, model designed for general purpose image-to-image translation.
The approach was presented by Phillip Isola, et al. in their 2016 paper noscriptd “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.
The GAN architecture is comprised of a generator model for outputting new plausible synthetic images and a discriminator model that classifies images as real (from the dataset) or fake (generated). The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.
Link
🔭 @DeepGravity
MachineLearningMastery.com
How to Implement Pix2Pix GAN Models From Scratch With Keras - MachineLearningMastery.com
The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products.…
Learning #ReinforcementLearning: #REINFORCE with #PyTorch!
Getting started with #PolicyGradients
Link
🔭 @DeepGravity
Getting started with #PolicyGradients
Link
🔭 @DeepGravity
Medium
Learning Reinforcement Learning: REINFORCE with PyTorch!
The REINFORCE algorithm is one of the first policy gradient algorithms in reinforcement learning and a great jumping off point to get into…