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.
FactorGCL uses hypergraph learning to predict stock returns by mining hidden factors.
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model…
DISTANA improves weather prediction by inferring hidden factors from temperature data.
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allo…
To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a connection weight matrix and the nonnegative real hidden units of the next layer. The PGBN's hidden layers are jointly trained with an upwar…
End-to-end speaker recognition method using neural networks.
To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer. The GBN's hidd…
Reversible RNNs reduce memory usage in training without sacrificing performance.
Deep models improve factor analysis by capturing non-linearity and interaction effects.
This paper solves a financial control problem with hidden factors using backward SDEs.
Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies …
We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions. This problem can be viewed as a simplification of the deep learning problem where finding a factorization corresponds to finding edges in different layers and valu…
Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.
Proposes MV-Co-VH for multi-view clustering using visible and hidden views.
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
Tensor-networks enhance probabilistic modeling in physics and machine learning.
A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its state variable is studied under partial information: the mean-reverting level and the mean-reverting speed parameters are modeled as hidden/unobservable stochastic variables. No-arbitrage pricing formulas for derivative securities written on a …
Bayesian tensor factorization approximates a complex tree model.
DeepUnHide uses deep learning to reveal hidden demographic features in recommender systems.
Hidden Markov Models analyze mobile health data to identify APNS states.
New method estimates treatment effects in time series data with hidden confounders.
Tensor factorization uncovers hidden patterns in student behavior data.
LP-SparseMAP relaxes SparseMAP for more complex structures.
RL agents learn from a few tasks to generalize to new ones.
Factorial hidden Markov models (FHMMs) are powerful tools of modeling sequential data. Learning FHMMs yields a challenging simultaneous model selection issue, i.e., selecting the number of multiple Markov chains and the dimensionality of each chain. Our main contribution is to address this model selection issue by exte…
A neural network method determines the latent dimensionality of NMF.
Debias recommender systems by accounting for hidden confounders using network information.
Study uses MLP models to predict large-cap US stocks, finding 2-3 hidden layers more flexible.
We solve a high-dimensional model where nonlinear autoencoders detect hidden structure missed by PCA.
A new VAR model with low-rank constraint for high-dimensional correlated series.
New algorithms for high-dimensional HMMs reduce complexity by discarding non-local factors.
This work speeds up fHMM analysis by tensor algebra.
Despite their increasing popularity and success in a variety of supervised learning problems, deep neural networks are extremely hard to interpret and debug: Given and already trained Deep Neural Net, and a set of test inputs, how can we gain insight into how those inputs interact with different layers of the neural ne…
DSCF-Net learns deep features for clustering with robustness and locality preservation.
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features a…
CVAE learns disentangled and coupled representations without prior knowledge.
spex-LVM infers interpretable latent factors from biomedical data.
Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lo…
A new method detects hidden driving forces in systems with multiple observables.
We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our a…
Individual risk models need to capture possible correlations as failing to do so typically results in an underestimation of extreme quantiles of the aggregate loss. Such dependence modelling is particularly important for managing credit risk, for instance, where joint defaults are a major cause of concern. Often, the d…
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data is modeled as a linear superposition, , of a potentially infinite number of hidden factors, . The Indian Buffet Process (IBP) is used as a prior on to incorporate sparsity and to …
This paper presents a novel approach to speaker subspace modelling based on Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is based on the idea of shared factors as in the Probabilistic Linear Discriminant Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors, herein t…
Deep neural networks can handle high dimensions without losing accuracy.
GLFA improves latent factor analysis by incorporating graph structures for HiDS matrices.
Bayesian NMF model improves predictions and avoids overfitting.
We describe a model for capturing the statistical structure of local amplitude and local spatial phase in natural images. The model is based on a recently developed, factorized third-order Boltzmann machine that was shown to be effective at capturing higher-order structure in images by modeling dependencies among squar…
In many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-va…