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.
Generative LLE modifies LLE to generate stochastic embeddings.
The tree reconstruction problem is to collect and analyze massive data at the th level of the tree, to identify whether there is non-vanishing information of the root, as goes to infinity. Its connection to the clustering problem in the setting of the stochastic block model, which has wide applications in machin…
Single linear solve combines surface reconstruction and uncertainty quantification.
Deep learning scheme identifies and reconstructs chaotic and stochastic systems from noisy data.
New method learns stochastic process representations without exact reconstruction.
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learning may improve the generalization ability of the network. In this paper, we propose that the generalization ability of an encoder-decoder n…
This work connects LLE, factor analysis, and probabilistic PCA through a stochastic perspective.
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
Deep learning models have shown state-of-the-art performance in many inverse reconstruction problems. However, it is not well understood what properties of the latent representation may improve the generalization ability of the network. Furthermore, limited models have been presented for inverse reconstructions over ti…
The labeled stochastic block model is a random graph model representing networks with community structure and interactions of multiple types. In its simplest form, it consists of two communities of approximately equal size, and the edges are drawn and labeled at random with probability depending on whether their two en…
Cryo-EM reconstruction is reformulated as a stochastic inverse problem to handle structural heterogeneity.
Study shows how numerical discretization affects reconstructions and parameter distributions in nano metrology.
New algorithm reconstructs genealogies from genetic data.
This study uses CNN-IOs to estimate MRI image reconstruction performance bounds.
Bayesian framework optimizes 3D view selection for specific tasks.
A scalable GPLVM model using stochastic variational inference.
We present a Bayesian method for feature selection in the presence of grouping information with sparsity on the between- and within group level. Instead of using a stochastic algorithm for parameter inference, we employ expectation propagation, which is a deterministic and fast algorithm. Available methods for feature …
Develops a new method to create object models from medical images.
This work combines deep learning and sparse coding for CT image reconstruction.
A new algorithm reconstructs population dynamics from coarse samples.
We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local consid…
Grad-TTS models speech from text using diffusion probabilistic techniques.
Paper extracts features from time series to improve forecasting accuracy.
Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities. Knowledge of veracious transmission probabilities is essential for prediction, optimization, and control of diffusion dynamics. Unfortunately, in most ca…
VCAE improves autoencoder quality on MNIST and CelebA.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
We consider inpainting in an unsupervised setting where there is neither access to paired nor unpaired training data. The only available information is provided by the uncomplete observations and the inpainting process statistics. In this context, an observation should give rise to several plausible reconstructions whi…
Scalable model checking for stochastic systems using Gaussian Processes and Bayesian Neural Networks.
A semi-supervised framework using stochastic interpolation and latent representations.
Paper analyzes PSGLD for adaptive IRL with finite-sample bounds.
Proposes flexible auto-encoders for varying data dimensions.
We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned {\em in situ} from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is develope…
CNPs improve function approximation by contrastive learning.
Based on criteria of mathematical simplicity and consistency with empirical market data, a stochastic volatility model is constructed, the volatility process being driven by fractional noise. Price return statistics and asymptotic behavior are derived from the model and compared with data. Deviations from Black-Scholes…
New method generates clean data from corrupted observations.
The study uses Markov chains to forecast cryptocurrency market dynamics.
FM4PDE learns PDE solutions from sparse data.
New algorithm reconstructs sparse networks in subquadratic time.
A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is developed to reconstruct an unknown -dimensional real-/complex-valued signal from `phaseless' quadratic equations of the form . This problem, also known as phase retrieval from magnitude-onl…
The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, …
We model non-stationary volume-price distributions with a log-normal distribution and collect the time series of its two parameters. The time series of the two parameters are shown to be stationary and Markov-like and consequently can be modelled with Langevin equations, which are derived directly from their series of …
New method learns dynamics from sparse data using geometric constraints.
In recent years Variation Autoencoders have become one of the most popular unsupervised learning of complicated distributions.Variational Autoencoder (VAE) provides more efficient reconstructive performance over a traditional autoencoder. Variational auto enocders make better approximaiton than MCMC. The VAE defines a …
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Euclidean space to obtain a low-dimensional representation, and then cluster the vertices into communities. We next employ nonparametric graph in…
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sha…
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
Method learns model for unknown stochastic system from data.
We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighte…