​Independent Component Analysis based on multiple data-weighting. arxiv.org/abs/1906.00028

🔗 Independent Component Analysis based on multiple data-weighting
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis (MWeICA) algorithm, a new ICA method which is based on approximate diagonalization of weighted covariance matrices. Our idea is based on theoretical result, which says that linear independence of weighted data (for gaussian weights) guarantees independence. Experiments show that MWeICA achieves better results to most state-of-the-art ICA methods, with similar computational time.

Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations

https://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html

​Moneyball — Linear Regression

🔗 Moneyball — Linear Regression
Using Linear Regression in Python to predict baseball season performance.

🎥 Childhood's End: Maturation of Deep Speech and Common Voice
👁 2 раз ⏳ 1048 сек.
#reworkDL

This presentation took place at the Deep Learning Summit, Boston on the 23 & 24 May. This presentation was given by Kelly Davis, Machine Learning Researcher at Mozilla.

More presentations and interviews from the Deep Learning Summit can be seen here on the Video Hub: https://videos.re-work.co/events/59-deep-learning-summit-boston-2019

We’ll talk about the blossoming of Deep Speech, an open deep learning based speech-to-text engine, and Common Voice, an open crowd-sourced speech corpora. We will

​DeepMind Made a Math Test For Neural Networks

https://arxiv.org/abs/1904.01557

🔗 Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and symbol manipulation rules. In this paper, we present a new challenge for the evaluation (and eventually the design) of neural architectures and similar system, developing a task suite of mathematics problems involving sequential questions and answers in a free-form textual input/output format. The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes. Having described the data generation process and its potential future expansions, we condu

​Advanced Topics in Deep Convolutional Neural Networks

🔗 Advanced Topics in Deep Convolutional Neural Networks
Residual networks, saliency maps, dilated convolutions, and more.

​Private AI — Federated Learning with PySyft and PyTorch

🔗 Private AI — Federated Learning with PySyft and PyTorch
An application to SMS spam detection with a GRU model

🎥 4 Ways to Use Machine Learning for Mobile
👁 1 раз ⏳ 3020 сек.
Learn more about AWS Startups at – https://amzn.to/2WG04um 
Machine Learning at the edge is about making your apps smarter with real-time object and face recognition, offline predictions, and protecting user privacy. In this session, we'll explore the four ways to handle prediction or inference and decision making in modern apps. Demystify deep learning and easily call managed ML services, build, train, and/or deploy ML models to mobile and IoT devices.

🎥 An Introduction to Deep Learning
👁 1 раз ⏳ 2627 сек.
Learn more about AWS Startups at – https://amzn.to/2WhgEwo 
Getting started with deep learning can feel really intimidating. In this session we'll dive right in to explaining the basic concepts of deep learning with barely any jargon and a small amount of arithmetic. We'll see how easy it is to take an existing pre-trained general-purpose image classification model from the cloud and re-train it to identify objects that we want the computer to recognize. We'll show how to do all of this with python, using a

🎥 My Experience With Deep Learning and TensorFlow | Nathan Hadjiyski | TEDxYouth@AnnArbor
👁 1 раз ⏳ 674 сек.
Nathan Hadjiyski is a 10th grader at Pioneer High School. He has been interested in science from young age and his passion for it continues to grow. He is trying to benefit society with his cancer research, and he hopes it could ultimately help patients get a more accurate diagnosis and as a result more adequate treatment. Nathan Hadjiyski’s presentation is about his experience with Deep Learning and Tensor Flow applied to kidney cancer diagnosis. Nathan Hadjiyski is a 10th grader at Pioneer High School. He

🎥 Обзор ICLR 2019
👁 1 раз ⏳ 3171 сек.
Девятого мая закончилась очередная International Conference on Learning Representations (ICLR). Мы сделаем обзор публикаций с ICLR, которые нам показались наиболее интересными.

Докладчик: Рауф Курбанов.

Ссылка на слайды: https://research.jetbrains.org/files/material/5cf7a348264f3.pdf

Автор поделился крутым опытом по разработке ботов для Слака на Питоне https://bit.ly/2WeNb67

​AI Replaces Human Appraisers stardate 2019.420

🔗 AI Replaces Human Appraisers stardate 2019.420
All the data that matters:

​ARTificial: на заре искусственного интеллекта

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

Как думаете, может ли искусственный интеллект творить? Или же он просто бездушная машина, способная лишь копировать?

За последние 10 лет произошел колоссальный прорыв в развитии искусственного интеллекта. Созданный человеком алгоритм прошел путь от простого распознавания образов до побед в самых разнообразных играх. Однако одна из самых эмоциональных и экспрессивных сфер деятельности человека – искусство – ему все еще неподвластна. Или нет? Это мы и предложили решить гостям закрытой выставки, которая расположилась в Музее русского импрессионизма на один день 29 мая.

🔗 ARTificial: на заре искусственного интеллекта
Как думаете, может ли искусственный интеллект творить? Или же он просто бездушная машина, способная лишь копировать? За последние 10 лет произошел колоссальный...

​Depth Estimation on Camera Images using DenseNets

🔗 Depth Estimation on Camera Images using DenseNets
Doing cool things with data!

​New interesting paper to read, on face generation(faster then GANs)
https://arxiv.org/abs/1906.00446

🔗 Generating Diverse High-Fidelity Images with VQ-VAE-2
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and decoder networks, making our model an attractive candidate for applications where the encoding and/or decoding speed is critical. Additionally, VQ-VAE requires sampling an autoregressive model only in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images. We demonstrate that a multi-scale hierarchical organization of VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.

​https://youtu.be/pgaEE27nsQw

Кто нибудь знает название пироги, либо аналоги в свободном доступе ?

🔗 Flexible Muscle-Based Locomotion for Bipedal Creatures
We present a control method for simulated bipeds, in which natural gaits are discovered through optimization. No motion capture or key frame animation was used in any of the results. For more information, see https://goatstream.com/research/papers/SA2013

Unsupervised Object Segmentation by Redrawing
https://arxiv.org/abs/1905.13539

🎥 ИИ и машинное обучение Рустам Гафаров Тренды в машинном обучении
👁 1 раз ⏳ 4970 сек.

​Alexa, Alex, or Al?

🔗 Alexa, Alex, or Al?
3 Suggestions to Fight Gender Biases in AI Assistants