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.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
Study models extreme skew surges along French Atlantic coast.
Study predicts future hospitalizations to manage COVID-19 patient surge.
Neural network predicts vessel motions with high accuracy.
BreakGPT predicts asset price surges using LLMs.
Research predicts XRP price anomalies using graph topologies.
The study finds generalized torsion elements in 3-manifolds from specific knot surgeries.
Paper improves prediction sets for distribution shifts without labels.
This paper presents a model of the dynamics of the wage income distribution.
Exchanges acquire excess processing capacity to accommodate trading activity surges associated with zero-sum high-frequency trader (HFT) "duels." The idle capacity's opportunity cost is an externality of low-latency trading. We build a model of decentralized exchanges (DEX) with flexible capacity. On DEX, HFTs acquire …
Study improves trading decisions by predicting profit and loss outcomes.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
Survey on calibration in machine learning, viewing it as indistinguishability.
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges …
PsychFM predicts individual gambling choices using psychological and machine learning models.
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining th…
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such cont…
The prominent inequality of wealth and income is a huge concern especially in the United States. The likelihood of diminishing poverty is one valid reason to reduce the world's surging level of economic inequality. The principle of universal moral equality ensures sustainable development and improve the economic stabil…
EMDLOT predicts bond defaults better than traditional methods.
The worldwide surge of multiresistant microbial strains has propelled the search for alternative treatment options. The study of Protein-Protein Interactions (PPIs) has been a cornerstone in the clarification of complex physiological and pathogenic processes, thus being a priority for the identification of vital compon…
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large numbe…
Study optimizes climate adaptation strategies for NYC.
The paper proposes DEA to make graph neural networks fairer in link prediction.
The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predictin…
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
Researchers introduce a method to assess the safety of interpretable machine learning models.
APGE protects graph node representations from inference attacks.
Develops variational Bayesian neural network for complex biomedical applications.
Study finds stock prices rarely appreciate during capital inflows but often appreciate during normal flows.
We analyze 27 house price indexes of Las Vegas from Jun. 1983 to Mar. 2005, corresponding to 27 different zip codes. These analyses confirm the existence of a real-estate bubble, defined as a price acceleration faster than exponential, which is found however to be confined to a rather limited time interval in the recen…
One-pass SGD converges in overparametrized neural networks with random data.
SAM optimizer benefits from normalization, stabilizing and guiding optimization.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
Mathematical framework for differential machine learning in finance.
A new particle filter uses diffusion models to improve state estimation from noisy data.
A new embedding method extracts dataset-scale metric distribution into vectorial representation for graph data.
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differe…
New method optimizes treatment policies to avoid winner's curse.
Work proposes CLAIRE to achieve counterfactual fairness from observational data without causal models.
Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco…
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
Researchers develop method to protect against 'weight poisoning' attacks on pre-trained models.
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers. However, speech, signal and audio data are naturally complex-valued after Fourier Transform, and studies have shown a potentially richer representation of complex nets. In …
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to lear…
New theory explains how equivariant self-supervised learning improves feature extraction.
As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpr…
The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability. Unlike the human visual system which is able to generalize robustly and learn with little supervision, DNNs normally require a massive amount of data to learn new concepts. In addition…
There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data. Despite being structurally richer and more robust than parametric ranking models, permutation-based models are less well understood statistically and generally lack efficient learning algorithms. In…