Finding Patterns of Monotonicity and Convexity in Data (DIconvex)Given an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic an …

Easy Plotting of Periodic Data with ‘ggplot2’ (ggperiodic)Implements methods to plot periodic data in any arbitrary range on the fly.

An overview of feature selection strategies

Point Completion Network (PCN) Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset. …

Deep learning models often have more parameters than observations, and still perform well. This is sometimes described as a paradox. In this work, we show experimentally that despite their huge number of parameters, deep neural networks can compress the data losslessly even when taking the cost of encoding the parameters into account. Such a compression viewpoint originally motivated the use of variational methods in neural networks. However, we show that these variational methods provide surprisingly poor compression bounds, despite being explicitly built to minimize such bounds. This might explain the relatively poor practical performance of variational methods in deep learning. Better encoding methods, imported from the Minimum Description Length (MDL) toolbox, yield much better compression values on deep networks, corroborating the hypothesis that good compression on the training set correlates with good test performance. Do Deep Learning Models Have Too Many Parameters An Information Theory Viewpoint

[image of cat lifting weights]

1) Stan

I’ve ended up (almost accidentally) collecting a number of different solutions to the “use a column to choose values from other columns in R” problem.

InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics

At the end of July, the Pentagon announced a change to the time period for conducting background investigations to help reduce the huge backlog of people waiting for their government clearance. It plans to simply do reinvestigations less often, officially stretching the process from five to six years, when in reality, it can already take much longer than that. While this may free up some resources to conduct initial investigations, it is a partial solution at best.