The Future of Deep Learning Is #Unsupervised, AI Pioneers Say

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AI on steroids: Much bigger neural nets to come with new hardware, say Bengio, Hinton, and LeCun

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Growing Neural Cellular Automata
Differentiable Model of Morphogenesis

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Invisible cloak of Harry Potter with python

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GitHub

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Two nice tools for drawing deep nets for your papers/presentations:

alexlenail

PlotNeuralNet

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Complete Statistical Theory of Learning (Vladimir #Vapnik) | MIT Deep Learning Series

YouTube

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#Position

Dear all,

Vivacity Labs, a fast-growing startup in London, is looking for a Machine Learning Researcher to work on using Reinforcement Learning for Traffic Signal Control. You would have extensive simulations & existing datasets to work with, and would be deploying your technology to live junctions in the UK from day 1.

For more details, please see:

https://angel.co/company/vivacity-labs/jobs/664275-machine-learning-researcher

Please feel free to apply directly to joinus@vivacitylabs.com with a CV and covering note.

Kind regards,
Mark

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#NeurIPS2020 Review Process

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#Fun

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Stanford CS330: Deep Multi-Task and #MetaLearning

cs330.stanford.edu

Lecture Videos:
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Eastern European Machine Learning Summer School
6-11 July 2020, Krakow, Poland

Deep Learning and Reinforcement Learning

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Audio Data Analysis Using Deep Learning with Python (Part 1) (Part 2)

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اگر با کراس کد زده باشید و اندازه کار بزرگ بوده باشه میدونید که یک سری محدودیت‌های خسته کننده برای
distributed training
وجود داره، البته که می‌شه مشکلات رو حل کرد اما خب زمانبر هست

توی تنسورفلو یک، ،submodule دیگری هم وجود داره به اسم estimator که توی نسخه ۲ توجه خوبی بهش شده و پیشرفتای خوبی داشته، حتی خیلی ریکامند می‌شه که موقع
Distributed Processing
بجای کراس ازین مورد استفاده بشه، ولی چون نوع syntax خودش رو داره برای کدهای بزرگ که روی کراس نوشته شده بنظر مفید نمیاد
اما :
tf.keras.estimator.model_to_estimator()
مشکل رو حل می‌کنه؛ توی تست‌های بنده اگر از خود کراس مستقیم استفاده کنید
pip install keras
و بخواید اینکارو انجام بدید مشکلاتی پیش میاد اما نسخه تنسورفلو ۲ به راحتی و عالی اینکارو انجام میده، دوم اینکه دیگه نیازی نیس یادتون بمونه که حتماً از
@tf.function
استفاده کنید، چون این مورد خوردش بهینه‌ساز‌ی‌هارو انجام میده، پردازش توزیع شده هم که دلیل اصلی استفاده هست

Statistical Modelling vs Machine Learning

At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

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Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual or automated hyperparameter searches in order to perform well on a new domain. This need is particularly acute in modern deep RL architectures which often incorporate many modules and multiple loss functions. In this paper, we take a step towards addressing this issue by using metagradients (Xu et al., 2018) to tune these hyperparameters via differentiable cross validation, whilst the agent interacts with and learns from the environment. We present the Self-Tuning Actor Critic (STAC) which uses this process to tune the hyperparameters of the usual loss function of the IMPALA actor critic agent(Espeholt et. al., 2018), to learn the hyperparameters that define auxiliary loss functions, and to balance trade offs in off policy learning by introducing and adapting the hyperparameters of a novel leaky V-trace operator. The method is simple to use, sample efficient and does not require significant increase in compute. Ablative studies show that the overall performance of STAC improves as we adapt more hyperparameters. When applied to 57 games on the Atari 2600 environment over 200 million frames our algorithm improves the median human normalized score of the baseline from 243

Paper

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Attention Augmented Convolutional Networks

Convolutional neural networks have proven to be a powerful tool for image recognition, allowing for ever-improving results in image classification (ImageNet), object detection (COCO), and other tasks. Despite their success, convolutions are limited by their locality, i.e. their inability to consider relations between different areas of an image. On the other hand, a popular mechanism which has proven success in overcoming locality is self-attention, which has shown to be able to capture long-range interactions (e.g. Show, Attend and Tell).

Article

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#probability and Coronavirus

From #statistical point of view, there is an important difference between having any sort of direct or indirect interaction with a person with #coronavirus and risk of getting the disease. For example, people sitting in window seats have lower chance of getting the disease if there would be a person with the virus in a flight (check the figure). Or if the person with the disease walk and have direct or indirect interaction with other people, it is mainly the people in two seats around him that have the high probability of getting the disease.

These are just some statistical facts but anyway we have to be very cautious.
Note. Don’t avoid interaction with people based on race.

Credit goes to Seyed Ali Madani

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Google's DeepMind just shared AI-generated predictions about the #coronavirus that could help researchers stem the global outbreak

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Exponential growth and epidemics

#coronavirus

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RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

Outlier detection is an important task in data mining and many technologies have been explored in various applications. However, due to the default assumption that outliers are non-concentrated, unsupervised outlier detection may not correctly detect group anomalies with higher density levels. As for the supervised outlier detection, although high detection rates and optimal parameters can usually be achieved, obtaining sufficient and correct labels is a time-consuming task. To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy. First, we propose a novel detection model Dual-GAN, which can directly utilize the potential information in identified anomalies to detect discrete outliers and partially identified group anomalies simultaneously. And then, considering the instances with similar output values may not all be similar in a complex data structure, we replace the two MO-GAN components in Dual-GAN with the combination of RCC and M-GAN (RCC-Dual-GAN). In addition, to deal with the evaluation of Nash equilibrium and the selection of optimal model, two evaluation indicators are created and introduced into the two models to make the detection process more intelligent. Extensive experiments on both benchmark datasets and two practical tasks demonstrate that our proposed approaches (i.e., Dual-GAN and RCC-Dual-GAN) can significantly improve the accuracy of outlier detection even with only a few identified anomalies. Moreover, compared with the two MO-GAN components in Dual-GAN, the network structure combining RCC and M-GAN has greater stability in various situations.

Paper

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Advancements in Reinforcement Learning with Sergey Levine

Podcast

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