#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
Reviews of Modern Physics
Machine learning and the physical sciences
In October 2018 an APS Physics Next Workshop on Machine Learning was held in Riverhead, NY. This article reviews and summarizes the proceedings of this very broad, emerging field.This needs to be a placard in the left-hand column, with a custom tag.
#DeepSpeech 0.6: Mozilla’s #Speech_to_Text Engine Gets Fast, Lean, and Ubiquitous
The #MachineLearning team at #Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models.
Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we’ll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.
Link
🔭 @DeepGravity
The #MachineLearning team at #Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models.
Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we’ll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.
Link
🔭 @DeepGravity
Mozilla Hacks – the Web developer blog
DeepSpeech 0.6: Mozilla’s Speech-to-Text Engine Gets Fast, Lean, and Ubiquitous
The Machine Learning team at Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. ...
Tune #Hyperparameters for Classification #MachineLearning Algorithms
The seven classification algorithms we will look at are as follows:
Logistic Regression
Ridge Classifier
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Bagged Decision Trees (Bagging)
Random Forest
Stochastic Gradient Boosting
Article
🔭 @DeepGravity
The seven classification algorithms we will look at are as follows:
Logistic Regression
Ridge Classifier
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Bagged Decision Trees (Bagging)
Random Forest
Stochastic Gradient Boosting
Article
🔭 @DeepGravity
The Pros and Cons of Using #JavaScript for #MachineLearning
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
DLabs
The Pros and Cons of Using JavaScript for Machine Learning - DLabs
There’s a misconception in the world of machine learning (ML) Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. Python for its simplicity:…
Deep Speech, a good #Persian podcasts about #AI
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
Link
🔭 @DeepGravity
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
Link
🔭 @DeepGravity
Castbox
Deep Speech | Listen Free on Castbox.
We will talk about artificial intelligence, machine learning and deep learning news.Millions of podcasts for all topics. Listen to the best free podcast...
#MachineLearning Algorithm Cheat Sheet for #Azure Machine Learning designer
#Microsoft
Link
🔭 @DeepGravity
#Microsoft
Link
🔭 @DeepGravity
Docs
Machine Learning Algorithm Cheat Sheet - designer - Azure Machine Learning
A printable Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for your predictive model in Azure Machine Learning designer.
Prediction of Physical Load Level by #MachineLearning Analysis of Heart Activity after Exercises
Paper
🔭 @DeepGravity
Paper
🔭 @DeepGravity
Improving Out-of-Distribution Detection in #MachineLearning Models
Link
#Google Research
🔭 @DeepGravity
Link
#Google Research
🔭 @DeepGravity
Google AI Blog
Improving Out-of-Distribution Detection in Machine Learning Models
Posted by Jie Ren, Research Scientist, Google Research and Balaji Lakshminarayanan, Research Scientist, DeepMind Successful deployment o...
secml: A #Python Library for Secure and Explainable #MachineLearning
We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at https://gitlab.com/secml/secml.
Paper
🔭 @DeepGravity
We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including not only test-time evasion attacks to generate adversarial examples against deep neural networks, but also training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and of the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0, and hosted at https://gitlab.com/secml/secml.
Paper
🔭 @DeepGravity
GitLab
Secure Machine Learning / SecML · GitLab
A Python library for Secure and Explainable Machine Learning Documentation available @ https://secml.gitlab.io Follow us on Twitter @
During the last two days, some famous #MachineLearning researchers elucidated their own definition of #DeepLearning. You might check the related links to read full definitions and discussions on each.
Yann LeCun:
#DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization. That's it.
This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.
https://www.facebook.com/722677142/posts/10156463919392143/
Andriy Burkov:
Looks like in late 2019, people still need a definition of deep learning, so here's mine: deep learning is finding parameters of a nested parametrized non-linear function by minimizing an example-based differentiable cost function using gradient descent.
https://www.linkedin.com/posts/andriyburkov_looks-like-in-late-2019-people-still-need-activity-6615377527147941888-ce68/
François Chollet:
Deep learning refers to an approach to representation learning where your model is a chain of modules (typically a stack / pyramid, hence the notion of depth), each of which could serve as a standalone feature extractor if trained as such.
https://twitter.com/fchollet/status/1210031900695449600
Link
🔭 @DeepGravity
Yann LeCun:
#DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization. That's it.
This definition is orthogonal to the learning paradigm: reinforcement, supervised, or self-supervised.
https://www.facebook.com/722677142/posts/10156463919392143/
Andriy Burkov:
Looks like in late 2019, people still need a definition of deep learning, so here's mine: deep learning is finding parameters of a nested parametrized non-linear function by minimizing an example-based differentiable cost function using gradient descent.
https://www.linkedin.com/posts/andriyburkov_looks-like-in-late-2019-people-still-need-activity-6615377527147941888-ce68/
François Chollet:
Deep learning refers to an approach to representation learning where your model is a chain of modules (typically a stack / pyramid, hence the notion of depth), each of which could serve as a standalone feature extractor if trained as such.
https://twitter.com/fchollet/status/1210031900695449600
Link
🔭 @DeepGravity
#MachineLearning from a Continuous Viewpoint
We present a continuous formulation of machine learning, as a problem in the calculus of variations and differential-integral equations, very much in the spirit of classical numerical analysis and statistical physics. We demonstrate that conventional machine learning models and algorithms, such as the random feature model, the shallow neural network model and the residual neural network model, can all be recovered as particular discretizations of different continuous formulations. We also present examples of new models, such as the flow-based random feature model, and new algorithms, such as the smoothed particle method and spectral method, that arise naturally from this continuous formulation. We discuss how the issues of generalization error and implicit regularization can be studied under this framework.
Paper
🔭 @DeepGravity
We present a continuous formulation of machine learning, as a problem in the calculus of variations and differential-integral equations, very much in the spirit of classical numerical analysis and statistical physics. We demonstrate that conventional machine learning models and algorithms, such as the random feature model, the shallow neural network model and the residual neural network model, can all be recovered as particular discretizations of different continuous formulations. We also present examples of new models, such as the flow-based random feature model, and new algorithms, such as the smoothed particle method and spectral method, that arise naturally from this continuous formulation. We discuss how the issues of generalization error and implicit regularization can be studied under this framework.
Paper
🔭 @DeepGravity
درود بر همهی شما دوستان گرامی،
امیدوارم این روزهای سخت بهاری به زودی با چیرگی سبزی بر سیاهی سپری بشه. هر چند اندوهش هرگز از یادها نخواهد رفت.
به منظور بررسی ابعاد بحران #کرونا از نگاه #ماشین_لرنینگ، قصد دارم به کمک شما عزیزان یک جلسهی هماندیشی آنلاین رو راهاندازی کنم. در لینک زیر زمانهای مختلفی رو میبینین. لطفا زمانی که برای شما مناسبتره رو انتخاب کنین که تو اون تایم از طریق زوم یا گوگل میت بتونیم دور هم جمع بشیم. سعی کردم گزینهها رو بین صبح و عصر و شب پخش کنم که با توجه به اختلاف ساعتها بتونیم تایم مشترکی رو پیدا کنیم:
https://doodle.com/poll/69fvgkegwq3y8p6w
هدف این جلسه بیشتر هم اندیشی و به اشتراک گذاری دانستهها و داشتهها ست. من خودم دو تا رپو آماده کردم که در موردشون توضیح خواهم داد.
(هدف مقاله دادن یا کار اقتصادی کردن نیست)
امیدوارم ما هم بتونیم در کنار تیم درمان، کمکی برای کشور (و شاید دنیا) در این شرایط باشیم.
اگه پیشنهادی هم دارین، لطفا در کامنت یا به صورت خصوصی پیام بذارین.
ارادتمند
#ai #computervision #machinelearning #deeplearning #covid19
@Reza
🔭 @DeepGravity
امیدوارم این روزهای سخت بهاری به زودی با چیرگی سبزی بر سیاهی سپری بشه. هر چند اندوهش هرگز از یادها نخواهد رفت.
به منظور بررسی ابعاد بحران #کرونا از نگاه #ماشین_لرنینگ، قصد دارم به کمک شما عزیزان یک جلسهی هماندیشی آنلاین رو راهاندازی کنم. در لینک زیر زمانهای مختلفی رو میبینین. لطفا زمانی که برای شما مناسبتره رو انتخاب کنین که تو اون تایم از طریق زوم یا گوگل میت بتونیم دور هم جمع بشیم. سعی کردم گزینهها رو بین صبح و عصر و شب پخش کنم که با توجه به اختلاف ساعتها بتونیم تایم مشترکی رو پیدا کنیم:
https://doodle.com/poll/69fvgkegwq3y8p6w
هدف این جلسه بیشتر هم اندیشی و به اشتراک گذاری دانستهها و داشتهها ست. من خودم دو تا رپو آماده کردم که در موردشون توضیح خواهم داد.
(هدف مقاله دادن یا کار اقتصادی کردن نیست)
امیدوارم ما هم بتونیم در کنار تیم درمان، کمکی برای کشور (و شاید دنیا) در این شرایط باشیم.
اگه پیشنهادی هم دارین، لطفا در کامنت یا به صورت خصوصی پیام بذارین.
ارادتمند
#ai #computervision #machinelearning #deeplearning #covid19
@Reza
🔭 @DeepGravity
Doodle
Doodle: The COVID-19 Aspects
For Iranians in AI
Forwarded from Apply Time Positions
🎓 Researcher in Machine Learning and AI for/against network security, OsloMet – Oslo Metropolitan University, #Norway
📚 Fields: #Algorithms #ArtificialIntelligence #ArtificialNeuralNetwork #ComputerCommunicationsNetworks #CyberSecurity #MachineLearning
⏳ Deadline: 2021-06-15
🔗 Link to the position
🔍 More positions
✔ @ApplyTime
🌐 https://applytime.ir
📚 Fields: #Algorithms #ArtificialIntelligence #ArtificialNeuralNetwork #ComputerCommunicationsNetworks #CyberSecurity #MachineLearning
⏳ Deadline: 2021-06-15
🔗 Link to the position
🔍 More positions
✔ @ApplyTime
🌐 https://applytime.ir
Forwarded from Apply Time Positions
🎓 Researcher in AI and Deepfake, OsloMet – Oslo Metropolitan University, #Norway
📚 Fields: #Algorithms #ArtificialIntelligence #ArtificialNeuralNetwork #MachineLearning #ComputerVision
⏳ Deadline: 2021-06-15
🔗 Link to the position
🔍 More positions
✔ @ApplyTime
🌐 https://applytime.ir
📚 Fields: #Algorithms #ArtificialIntelligence #ArtificialNeuralNetwork #MachineLearning #ComputerVision
⏳ Deadline: 2021-06-15
🔗 Link to the position
🔍 More positions
✔ @ApplyTime
🌐 https://applytime.ir