A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Self-supervised learning improves few-shot classification and segmentation on point clouds.
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods. The vast majority of remote sensing approaches either selectively choose single cloud-free observations or employ a pre-classification strategy to identify and mask cloudy pixels. We follow a different st…
Machine learning improves cloud cover forecasting.
A JAX toolbox solves optimal transport problems for point clouds and histograms.
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground cover. The aim is to derive a supervised learning model for the classification usin…
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for challenging tasks including 3D points generation, reconstruction, compression…
Study characterizes learning from heavy-tailed data in high dimensions using superstatistical methods.
Bi-filtration stabilizes TDA mapper results under noise.
New approach uses distributed persistence for stable, parallelizable topological analysis of large point clouds.
In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotati…
New method upsamples sparse, non-uniform point clouds more accurately.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…
Framework optimizes cloud container sizing for ML tasks.
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely o…
A novel method compresses point cloud attributes by folding them onto a 2D grid.
We proposed a novel graph convolutional neural network that could construct a coarse, sparse latent point cloud from a dense, raw point cloud. With a novel non-isotropic convolution operation defined on irregular geometries, the model then can reconstruct the original point cloud from this latent cloud with fine detail…
A novel method compares 3D point clouds using information geometry.
Develops a method for conformal parameterization of point clouds without fixed boundaries.
Algorithm classifies point clouds using deep set linearized optimal transport.
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task,…
This paper analyzes CNNs for malware detection in cloud IaaS.
Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.
Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While mas…
The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are of…
Point cloud is the most fundamental representation of 3D geometric objects. Analyzing and processing point cloud surfaces is important in computer graphics and computer vision. However, most of the existing algorithms for surface analysis require connectivity information. Therefore, it is desirable to develop a mesh st…
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" …
SPINN optimizes neural network inference on devices and cloud.
The paper transforms a convex hull into a concave surface around a point cloud.
Paper proposes a hierarchical approach to malware detection in cloud environments.
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution…
Deep learning identifies precipitation clouds from all-sky camera data.
In the following article we discuss Delaunay triangulations for a point cloud on an embedded surface in . We give sufficient conditions on the point cloud to show that the diagonal switch algorithm finds an embedded Delaunay triangulation.
The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in …
Method certifies edge predictions with cloud-level reliability.
The study proves strong cosmic censorship violation for spherically symmetric dust clouds.
Method computes harmonic and conformal maps from point clouds.
Generative model creates high-quality meshes from point clouds.
Estimator calculates surface curvature from point cloud samples.
Paper models cloud outages for cyber insurance stress-testing.
The concept of a Point Cloud has played an increasingly important role in many areas of Engineering, Science, and Mathematics. Examples are: LIDAR, 3D-Printing, Data Analysis, Computer Graphics, Machine Learning, Mathematical Visualization, Numerical Analysis, and Monte Carlo Methods. Entering point cloud into Google r…
LatticeNet segments 3D point clouds faster and more efficiently.
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical…
New method reduces cloud usage for mobile/IoT predictions.
New method improves Gaussian process regression on complex, sparse point clouds.
Improved road segmentation on low-res LIDAR data for autonomous vehicles.
Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point…
SCENE-Net improves 3D point cloud segmentation with low resource usage and transparency.