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.
New model combines shape and feature-based measures for better time series classification.
Consumer Demand Response (DR) is an important research and industry problem, which seeks to categorize, predict and modify consumer's energy consumption. Unfortunately, traditional clustering methods have resulted in many hundreds of clusters, with a given consumer often associated with several clusters, making it diff…
Shapelet transform improves time series classification for earthquake, wind, and wave events.
New algebraic rules for 5D shapes based on 3D cocycles.
Signatures provide a succinct description of certain features of paths in a reparametrization invariant way. We propose a method for classifying shapes based on signatures, and compare it to current approaches based on the SRV transform and dynamic programming.
Benchmark study evaluates 8 clustering methods on 99 UCR time series datasets.
Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on…
This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome the challenges in the field of seismology related to automated detection and cataloging of earthquakes. EQShapelets are amplitude and phase…
For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold. Usually, distances among samples are computed to capture an underlying data structure. Here we propose a metric according to angular changes along a geodesic line, thereby reflecting the underlying …
Recognizing facial expressions from static images or video sequences is a widely studied but still challenging problem. The recent progresses obtained by deep neural architectures, or by ensembles of heterogeneous models, have shown that integrating multiple input representations leads to state-of-the-art results. In p…
New method reconstructs 3D shapes from 2D images using Kendall's shape space.
Efficient method for shape modeling invariant to rigid motion.
Studying the impact of climate change on precipitation is constrained by finding a way to evaluate the evolution of precipitation variability over time. Classical approaches (feature-based) have shown their limitations for this issue due to the intermittent and irregular nature of precipitation. In this study, we prese…
This paper introduces a new shape-based image reconstruction technique applicable to a large class of imaging problems formulated in a variational sense. Given a collection of shape priors (a shape dictionary), we define our problem as choosing the right elements and geometrically composing them through basic set opera…
This paper proposes a new Nystrom-based clustering algorithm for large-scale data.
The study optimizes cell membranes' shapes based on curvature and proves existence of minimizers.
How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we lever…
Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determin…
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statisti…
We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale. As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models agains…
We consider the problem of modelling noisy but highly symmetric shapes that can be viewed as hierarchies of whole-part relationships in which higher level objects are composed of transformed collections of lower level objects. To this end, we propose the stochastic wreath process, a fully generative probabilistic model…
Improves CNN robustness by reducing texture bias.
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentations. When purely data driven segmentation produces poor results, the …
This research optimizes fluid-dynamic designs using deep learning and active learning.
Despite remarkable advances in automated visual recognition by machines, some visual tasks remain challenging for machines. Fleuret et al. (2011) introduced the Synthetic Visual Reasoning Test (SVRT) to highlight this point, which required classification of images consisting of randomly generated shapes based on hidden…
Improves functional linear regression with shape transfer learning.
Short-term road traffic prediction (STTP) is one of the most important modules in Intelligent Transportation Systems (ITS). However, network-level STTP still remains challenging due to the difficulties both in modeling the diverse traffic patterns and tacking high-dimensional time series with low latency. Therefore, a …
Study analyzes neural network models to understand generalization performance.
Study invariant measures on measured laminations for subgroups of mapping class group.
New set-valued star-shaped risk measures introduced for better risk assessment.
The Bergman measure converges to the Zhang measure on a hybrid space.
Bayesian approach to robust risk measures under model uncertainty.
Introduces Star-Shaped deviation measures for risk analysis.
The paper studies dynamic star-shaped risk measures and their representation.
Transformers can interpolate between arbitrary measures.
Classifies invariant measures on specific character varieties.
Paper characterizes star-shaped risk measures and their properties.
Paper introduces quasi-logconvex risk measures and their properties.
Submodularity is studied for convex risk measures, including Expected Shortfall.
New geometric measure simplifies complex analysis.
The paper explores non-convex risk measures and their characterizations.
The paper calculates extreme measures in continuous time conic finance.
One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be no study that compares fairness measures and feature importance measures. In this paper we propose ways to evaluate and compare such measure…
Paper characterizes monotonic mean-deviation risk measures.
Introduces factor risk measures to assess risk relative to multiple factors.
Dual representations for robust risk measures and uncertainty sets.
A scalable approach to learning from probability measures using quantization.
Study on measurable pseudo-Anosov maps on surfaces.