**TensorWatch**

TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data. TensorWatch is designed to be flexible and extensible so you can also build your own custom visualizations, UIs, and dashboards. Besides traditional ‘what-you-see-is-what-you-log’ approach, it also has a unique capability to execute arbitrary queries against your live ML training process, return a stream as a result of the query and view this stream using your choice of a visualizer (we call this Lazy Logging Mode).

Introducing TensorWatch: Microsoft Research New Tool for Debugging Deep Learning Programs … **SelectiveNet**

We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contrast, SelectiveNet is trained to optimize both classification (or regression) and rejection simultaneously, end-to-end. The result is a deep neural network that is optimized over the covered domain. In our experiments, we show a consistently improved risk-coverage trade-off over several well-known classification and regression datasets, thus reaching new state-of-the-art results for deep selective classification. … **Structured Uncertainty Prediction Network**

This paper is the first work to propose a network to predict a structured uncertainty distribution for a reconstructed image. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits efficient sampling and likelihood evaluation. We demonstrate that our model can accurately reconstruct ground truth correlated residual distributions for synthetic datasets and generate plausible high frequency samples for real face images. We also illustrate the use of these predicted covariances for structure preserving image denoising. … **Cronbach’s Alpha**

In statistics (classical test theory), Cronbach’s alpha is the trivial name used for tau-equivalent reliability \rho_T as a (lowerbound) estimate of the reliability of a psychometric test. Synonymous terms are: coefficient alpha, Guttman’s \lambda _{3}, Hoyt method and KR-20. Cronbach’s alpha reliability coefficient is one of the most widely used indicators of the scale reliability. It is used often without concern for the data (this will be a different text) because it is simple to calculate and it requires only one implementation of a single scale. The aim of this article is to provide some more insight into the functioning of this reliability coefficient without going into heavy mathematics. …

# If you did not already know

**02**
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Jun 2021

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