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.
Detects anomalies in multiple processes using hidden Markov models.
The paper uses Bayesian methods to infer hidden processes with unknown parameters.
The generic identification problem is to decide whether a stochastic process is a hidden Markov process and if yes to infer its parameters for all but a subset of parametrizations that form a lower-dimensional subvariety in parameter space. Partial answers so far available depend on extra assumptions on the pro…
New method uncovers hidden causal connections in multivariate point process networks.
Researchers calculate Shannon entropy rates of hidden Markov processes efficiently.
Modified asymmetric hidden Markov models for time series with autoregressive components.
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logis…
Study examines dependence properties of Bayesian neural network units in finite-width networks.
The Viterbi process can be extended indefinitely in a pairwise Markov model.
Study on hidden units in finite Bayesian neural networks and their tail properties.
New algorithm processes Riemannian data more efficiently.
A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. …
Paper reveals hidden convexities in deep learning models using sparse signal processing.
We consider a self-exciting counting process, the parameters of which depend on a hidden finite-state Markov chain. We derive the optimal filter and smoother for the hidden chain based on observation of the jump process. This filter is in closed form and is finite dimensional. We demonstrate the performance of this fil…
A new metric space model for point process excitations uncovers hidden interactions.
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear dynamical systems to an interactive group scenario. Various individual processes interact a…
A new model separates persistence and transition priors in HDP-HMM.
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussi…
This paper compares HMM and LSTM for time series forecasting.
Develops a more flexible HDP-HMM for temporal data segmentation.
New model improves cancer screening prediction accuracy.
DISTANA improves weather prediction by inferring hidden factors from temperature data.
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some i…
NoMoPy models noise as HMM/FHMM in Python.
Epsilon-machines are minimal, unifilar presentations of stationary stochastic processes. They were originally defined in the history machine sense, as hidden Markov models whose states are the equivalence classes of infinite pasts with the same probability distribution over futures. In analyzing synchronization, though…
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples. The hidden parameters determine the attributions of hidden predictors or the nonlinear mechanism of an estimator, while the bright parameters characterize how h…
Hidden Markov Neural Networks balance adaptation and forgetting in time-series data.
The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.
This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for E…
Paper proposes a new model and methods for robustly de-interleaving HMP mixtures.
We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes. The model parameters are learned by maximizing the observed-da…
Traditional Relational Topic Models provide a way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, link prediction, benefit from this revealed knowledge. However, existing relational topic models are based on an assumption t…
SNEPPPs use squared neural networks to efficiently model Poisson point processes.
Flexible log file parsing using HMM adapts to evolving content.
Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically with the dimensionality of state space. However, in practice, these large MDPs typically incorporate a latent or hidden low-dimensional structu…
Wide neural networks with narrow bottlenecks behave like deep Gaussian processes.
Given a collection of entities (or nodes) in a network and our intermittent observations of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawke…
A stochastic model with hidden discrete Markov processes is constructed to understand the behavior of debtors.
Hidden Markov Chains and Linear-chain CRFs are equivalent.
Learning and inferring features that generate sensory input is a task continuously performed by cortex. In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from natural images, written text, audio signals, etc. These networks usually involve de…
In this paper we consider a reduced-form intensity-based credit risk model with a hidden Markov state process. A filtering method is proposed for extracting the underlying state given the observation processes. The method may be applied to a wide range of problems. Based on this model, we derive the joint distribution …
Detect hidden confounding in observational data using multiple environments.
Investor selects portfolios based on news attention in a hidden Markov model.
Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an access to samples which have high quality, and advanced generative models are applied to generate samples from noisy training data through amb…
UNMIX identifies hidden buyers in darknet markets by clustering anonymized IDs.
Aggregates models from different datasets using shared latent structures.
Deep Gaussian processes reduce uncertainty in porous media flow modeling.