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.
Machine learning model diagnoses COVID-19 from routine blood tests.
Smart meters detect dementia patients' daily activities to prevent crises.
Testing the implementation of deep learning systems and their training routines is crucial to maintain a reliable code base. Modern software development employs processes, such as Continuous Integration, in which changes to the software are frequently integrated and tested. However, testing the training routines requir…
Dynamic memory prevents forgetting in continuous learning of medical images.
New method learns routines from inertial data without privacy concerns.
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly inter…
Lung segmentation accuracy varies little across diverse datasets.
In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…
Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative big-sample datasets on microanatomy, syn…
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
New method clusters financial time series into volatility regimes.
Reformer improves Transformer efficiency for long sequences.
The splitting number of a link is the minimal number of crossing changes between different components required to convert it into a split link. We obtain a lower bound on the splitting number in terms of the (multivariable) signature and nullity. Although very elementary and easy to compute, this bound turns out to be …
PSO optimizes model parameters in nonstandard distributions.
Optimizes reinforcement learning controllers for reliability.
The computation of convolution layers in deep neural networks typically rely on high performance routines that trade space for time by using additional memory (either for packing purposes or required as part of the algorithm) to improve performance. The problems with such an approach are two-fold. First, these routines…
The paper analyzes equity market dynamics and optimal portfolios using time-varying optimization.
We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and becaus…
Research proposes a risk-free machine learning model for COVID screening from routine blood tests.
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs). The library allows one to quickly create SPNs both from data and through a domain specific language (DSL). It e…
ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.
This paper analyzes how errors accumulate in PCA's deflation method.
TFCheck detects training issues in ML programs using TensorFlow.
The paper calibrates shrinkage covariance estimators for spectral functionals in high dimensions.
Improved model predicts interactions in complex systems better than previous methods.
Extends Gaussian process regression for non-Gaussian data.
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell …
Detects jumps in financial asset prices with U-shape volatility.
Real GDP growth rate in developed countries is found to be a sum of two terms. The first term is the reciprocal value of the duration of the period of mean income growth with work experience, Tcr. The current value of Tcr in the USA is 40 years. The second term is inherently related to population and defined by the rel…
While the channel capacity reflects a theoretical upper bound on the achievable information transmission rate in the limit of infinitely many bits, it does not characterise the information transfer of a given encoding routine with finitely many bits. In this note, we characterise the quality of a code (i. e. a given en…
Meta-learning improves feature extraction for few-shot tasks.
A new variational method improves deep neural network inference.
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present GapTV, an approach that …
Machine learning models detect COVID-19 from routine blood tests.
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present Maximum Variance Total V…
The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods to preexisting ones. While developing ideas, there is a temptatio…
Framework improves resilience in operations through joint long-term and short-term decision-making.
New method interprets quantum many-body snapshots for phase detection.
AdaVol adapts QML for real-time GARCH volatility prediction.
Global convergence for robust regression problems via IRLS with enhancements.
Optimizes CM for stochastic convex optimization with progressive precision.
We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the c…
Improved scalable machine learning under heavy-tailed data.
New algorithm updates eigenvectors of evolving graphs efficiently.
RL agents fail to generalize to unseen environments, even when dynamics are similar.
System guides freehand obstetric ultrasound probe movements.
Bayesian model detects internal bleeding in ICU patients.
Sparked by Alòs, León, and Vives (2007); Fukasawa (2011, 2017); Gatheral, Jaisson, and Rosenbaum (2018), so-called rough stochastic volatility models such as the rough Bergomi model by Bayer, Friz, and Gatheral (2016) constitute the latest evolution in option price modeling. Unlike standard bivariate diffusion models s…