arXiv research

Develops methods to simulate rare transitions in molecular systems.

A new method uses deep learning to efficiently sample rare transitions for estimating committor functions.

Deep reinforcement learning method finds rare events in complex systems.

The committor function is a central object of study in understanding transitions between metastable states in complex systems. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this paper,…

PHINN: A generative model for rare-event time series using persistent homology

Method reveals dissimilarity in alloys' Curie temperatures.

New methods use machine learning to simulate rare transitions in molecular systems.

Enhanced diffusion sampling improves rare event sampling in biomolecular simulations.

Enhanced diffusion sampling tackles rare event sampling in biomolecular simulations.

A new framework uses stochastic optimal control to estimate rare events more accurately.

We study the problem of predicting rare critical transition events for a class of slow-fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution and induces critical transitions. By taking advantage of recent advances in reservoir…

New method uses active importance sampling for rare event optimization in high-dimensional problems.

PRESTO improves rare event prediction by shrinking towards proportional odds model.

Develops methods for spectral estimation and rare-event prediction in complex systems.

New CVs preserve transition rates in molecular dynamics.

Study phase transitions in shuffled regression problems.

Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which i…

New insights into neural networks reveal unexpected phase transitions.

Develops a machine learning framework for computing most probable paths in stochastic systems.

Efficient RL algorithms for linear function approximation with limited adaptivity constraints.

The study of the critical dynamics in complex systems is always interesting yet challenging. Here, we choose financial market as an example of a complex system, and do a comparative analyses of two stock markets - the S&P 500 (USA) and Nikkei 225 (JPN). Our analyses are based on the evolution of crosscorrelation struct…

New RL algorithm achieves nearly optimal performance for linear MDPs.

Adjoint sampler targets infinite-dimensional function spaces for efficient sampling.

Study phase transitions in identifying infected individuals using group testing.

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural …

Proposes a thermodynamic work minimization framework for guiding generative models.

Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.

Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.

Generative model simulates rare events for better decision making.

We study the behavior of simple models for financial markets with widely spread frequency either in the trading activity of agents or in the occurrence of basic events. The generic picture of a phase transition between information efficient and inefficient markets still persists even when agents trade on widely spread …

AI boosts study of rare weather extremes with lower costs.

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning m…

Develops a method to estimate rare-event probabilities under distributional uncertainty.

Reduces false positives in classifying rare online platforms.

This paper evaluates data enrichment techniques for rare event detection in manufacturing.

Proposes a method to ensure accurate estimation of rare events in AI systems.

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rar…

WEST uses EHRs and expert cases to improve rare disease phenotyping.

In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth…

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease …

Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary betwee…

This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.

In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involve…

Simulates patient pathways to detect delayed rare disease diagnoses.

Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time series increases, motivating the development of sub-sampling-based approaches. These approximate the full posterior by using small random subsequ…

Model predicts travel time under rare conditions using a vector-space model.

Deep CNNs struggle with rare taxa; one-class classifiers help identify them.

Study compares resampling methods for rare event prediction in longitudinal studies.