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.
AR-Net models time-series with interpretable coefficients and scalability.
The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a…
Paper proposes ARPHMM for fault detection and prognosis in aero-engines.
Develops rMultiNet R package for multilayer network analysis.
SAMoSSA combines mSSA and AR for accurate time series analysis.
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation. The model considered can be regarded as a bivariate time-varying threshold autoregressive(AR) process for the hourly electric load and temperature. The joint modeling approach…
Communication costs, resulting from synchronization requirements during learning, can greatly slow down many parallel machine learning algorithms. In this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in which subsets of data are processed independently, with very little communication. First, w…
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning f…
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneousl…
We derive expressions for the predicitive information rate (PIR) for the class of autoregressive Gaussian processes AR(N), both in terms of the prediction coefficients and in terms of the power spectral density. The latter result suggests a duality between the PIR and the multi-information rate for processes with mutua…
Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique bas…
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
A video-based re-identification method using attention mechanisms.
Investigates optimal strategies for behavioral control problems with finite variation controls.
Uses news sentiment scores for direct reinforcement trading in financial markets.
Measures difficulty of predictions to improve deep learning models.
Timber targets decision trees, outperforming existing attacks.
Developed scalable ABM for complex financial markets.
Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, char…
How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day sc…
Cyclic Data Parallelism reduces memory usage and balances gradient communications.
Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), made it easier to train speech recognition systems in an end-to-end fashion. However in real-valued models, time frame components such as mel-filter-bank energies and the cepstral coeffic…
Statistical neurodynamics studies macroscopic behaviors of randomly connected neural networks. We consider a deep layered feedforward network where input signals are processed layer by layer. The manifold of input signals is embedded in a higher dimensional manifold of the next layer as a curved submanifold, provided t…
Stable algebraic filters improve neural network performance.
This paper reviews and benchmarks DVAEs for sequential data.
A new neural network separates singing voices more effectively.
Study improves cross-modal bike-share and transit demand prediction.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams. Unlike visible light, Radio Frequency (RF) signals penetrate opaque obstructions and reflect highly off humans. This paper establishes a deep-learning model that can be trained to reconstruct continuous vid…
Improved MLMC method for barrier options with non-Lipschitz coefficients.
Modeling complex systems with multi-resolution data and causal dependencies.
New diagnostic method detects misspecified models in inverse PDE problems.
Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over time, often in unforeseen ways. These variations are due to so-called con…
Automates fairness and accuracy optimization in deep learning models for tabular data.
We propose a simple imputation method for high-dimensional linear regression with missing data.
The problem of distributed representation learning is one in which multiple sources of information are processed separately so as to learn as much information as possible about some ground truth . We investigate this problem from information-theoretic grounds, through a generalization of Tishby's ce…
New study on time series anomaly detection shows overlapping inference improves performance.
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnos…
MAESTRO improves multimodal learning for dynamic time series with adaptive attention and robustness.
Transformers learn a mesa-optimizer to implement in-context learning.
Method detects batch heterogeneity in genomic data.
This work enables privacy-preserving model learning from single samples per client.
Paper proposes an EKF for estimating time-varying market efficiency.
LSTM model predicts rainfall runoff with high temporal resolution.
Paper predicts transaction confirmation time in Ethereum blockchain using machine learning.
Based on a recently proposed -dependent detrended cross-correlation coefficient , we generalize the concept of minimum spanning tree (MST) by introducing a family of -dependent minimum spanning trees (MST) that are selective to cross-correlations between different fluctuation amplitudes and different time…