ARIMA, Syria, and mapping The Cure: Enjoy these new, intriguing, and overlooked datasets and kernels.

We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family. An Information-Theoretic Analysis of Deep Latent-Variable Models

Hazelcast Hazelcast, a leading open source in-memory data grid (IMDG) with hundreds of thousands of installed clusters and over 17 million server starts per month, launched Hazelcast Jet – a distributed processing engine for big data streams. With Hazelcast’s IMDG providing storage functionality, Hazelcast Jet is a new Apache 2 licensed open source project that performs parallel execution to enable data-intensive applications to operate in near real-time. Using directed acyclic graphs (DAG) to model relationships between individual steps in the data processing pipeline, Hazelcast Jet is simple to deploy and can execute both batch and stream-based data processing applications. Hazelcast Jet is appropriate for applications that require a near real-time experience such as sensor updates in IoT architectures (house thermostats, lighting systems), in-store e-commerce systems and social media platforms. …

Data Scientist and author Siraj Raval recently released a 12-minute video overview of Azure Machine Learning (embedded at the end of this post). The video begins with a overview of cloud computing and Microsoft Azure generally, before getting into the details of some specific Azure services for machine learning:

A Practical Introduction to K-Nearest Neighbors Algorithm for Regression (with Python code)

Ever since my recent experience with 3-D printing, I’ve been itching for an excuse to print more. It’s a slow process that takes much longer than graphs rendered on a computer screen. But maybe that’s why it’s so satisfying.

Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers (pssmooth)Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treat …

Think of this as a standard Jupyter Notebook with slightly different aesthetics. You can write Python code and text (using Markdown syntax) just like in Jupyter and run the code completely in the cloud on Kaggle’s servers. However, Kaggle kernels have some unique features not available in Jupyter Notebook. Hit the leftward facing arrow in the upper right to expand the kernel control panel which brings up three tabs (if the notebook is not in fullscreen, then these three tabs may already be visible next to the code).

I recently attended a discussion at Fifth Elephant on privacy. During the panel, one of the panelists asked the audience: “how many of you are concerned about your privacy online, and take steps to protect it?”

Causal Information Theory – Formal Introduction of Key Concepts