Great summer read: FCOS (Fully Convolutional One-Stage object detection)
https://arxiv.org/abs/1904.01355
Simpler than the already simple RetinaNet architecture, with a couple of neat tricks. Pic below from paper, probably cherry-picked 😇 but still impressive. Every orange is boxed.

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Playing Go without Game Tree Search Using Convolutional Neural Networks.
http://arxiv.org/abs/1907.04658
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https://towardsdatascience.com/linear-regression-using-flavor-of-python-bcbdc616a944

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th the enstop package: https://github.com/lmcinnes/enstop #SciPy2019

For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing
7. Cognitive Neuroscience
8. Research Papers and Related Courses

https://t.me/DeepLearningML

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology.
http://arxiv.org/abs/1907.05008

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Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
https://deepai.org/publication/improving-deep-lesion-detection-using-3d-contextual-and-spatial-attention by Qingyi Tao et al.
#ComputerVision #PatternRecognition

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REINFORCE (Section 6 of Williams, 1992) *is* an ES, and is very closely related to OpenAI-ES, NES, CMA-ES.

Simple Random Architecture Search with hand-engineered components already gets ~ SOTA for CIFAR10, PTB

https://github.com/hardmaru/gecco-tutorial-2019 …
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Cheat sheet: TensorFlow, an open source software library for machine learning
https://www.techrepublic.com/article/tensorflow-googles-open-source-software-library-for-machine-learning-the-smart-persons-guide/ …
#machinelearning

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Jupyter Notebook Documentation

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Time-series analysis (TSA) is used in many fields, including finance, economics, meteorology and marketing.

Despite this, I occasionally hear the criticism that it doesn't actually work because these models assume normally distributed data and cannot handle non-linearities.

Academic researchers have studied time-series analysis for many decades, so this is not an obscure topic. A gigantic amount of research on TSA has been conducted and is public domain.

Time-series data are seldom linear, so the first objection does not make sense. Secondly, normality need not be assumed - this is a myth.

For those who'd like to learn more about TSA, two excellent introductions are:

- Time Series Analysis and Its Applications (Shumway and Stoffer)
- Time Series Analysis (Wei)

There are many others on specialized topics, such as:

- Multiple Time-Series Analysis (Lütkepohl)
- Time Series Analysis by State Space Methods (Durbin and Koopman)
- Time Series Modelling with Unobserved Components (Pelagatti)
- Hidden Markov Models for Time Series (Zucchini)
- GARCH Models (Francq and Zakoïan)
- Handbook of Volatility Models and Their Applications (Bauwens et al.)
- Nonparametric Econometrics (Li and Racine)

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Google Machine Learning Dictionary

https://developers.google.com/machine-learning/glossary/

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Developing the control of an exoskeleton with #Matlab & #Simulink to help patients relearn how to walk. The Statistics & Machine Learning, Curve Fitting Toolboxes were also used.

Students and Mechatronic engineers may want to check out the links in Comment-1 for a Youtube short tutorial on an exoskeleton designed with Simulink plus some research papers that I came across on the topic. There are many, but I simply list 3. See Comment-2 for the link to Lokomat's website (which is mentioned in the MathWorks blog article) to see their exoskeleton products.
https://blogs.mathworks.com/headlines/2017/06/28/robotic-exoskeleton-helps-patients-relearn-how-to-walk/

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Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.

Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.

The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.

Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.

Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.

They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
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Model evaluation techniques in one picture,
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10 ML Books that You Need To Consider

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Facebook & Carnegie Mellon build first AI that beats pros in 6-player Hold’em Poker. They tested Pluribus against professional poker players, including two winners of the World Series of Poker Main Event. Pluribus won decisively.

It uses Monte Carlo Counterfactual Regret Minimization algorithm which updates the traverser’s strategy by assessing the value of real and hypothetical moves.
In Pluribus, this traversal is actually done in a depth-first manner for optimization purposes.

This algorithm/bot is priceless!!
Link: https://lnkd.in/eHzfdsm
#artificialintelligence

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Deep learning based super resolution, without using a GAN

https://towardsdatascience.com/deep-learning-based-super-resolution-without-using-a-gan-11c9bb5b6cd5

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Size-free generalization boundsfor convolutional neural networks
https://arxiv.org/pdf/1905.12600.pdf

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Dopamine for RL Researchers.
https://github.com/google/dopamine

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R-Transformer: Recurrent Neural Network Enhanced Transformer "empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks"
https://arxiv.org/abs/1907.05572
https://github.com/DSE-MSU/R-transformer

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