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Found 20 papers, 9 papers with code
Learning of networked spreading models from noisy and incomplete data
Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data.
Forced oscillation source localization from generator measurements
Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations.
Learning Energy-Based Representations of Quantum Many-Body States
An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest.
Congruity of genomic and epidemiological data in modeling of local cholera outbreaks
Cholera continues to be a global health threat.
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware
This work builds on those insights and identifies a class of small hardware-native Ising models that are robust to noise effects and proposes a procedure for executing these models on QA hardware to maximize Gibbs sampling performance.
Combinatorial Optimization
Vocal Bursts Intensity Prediction
Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
Overall, the proposed QASA protocol provides a useful tool for assessing the performance of current and emerging quantum annealing devices.
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.
Learning Continuous Exponential Families Beyond Gaussian
We address the problem of learning of continuous exponential family distributions with unbounded support.
Programmable Quantum Annealers as Noisy Gibbs Samplers
Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning.
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting.
Learning of Discrete Graphical Models with Neural Networks
In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model.
Scalable Influence Estimation Without Sampling
The resulting saving of a potentially large sampling factor in the running time compared to simulation-based techniques hence makes it possible to address large-scale problem instances.
Real-time Anomaly Detection and Classification in Streaming PMU Data
Ensuring secure and reliable operations of the power grid is a primary concern of system operators.
Efficient Learning of Discrete Graphical Models
We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models.
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations.
Information Theoretic Optimal Learning of Gaussian Graphical Models
What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered?
Optimal structure and parameter learning of Ising models
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning.
Reconstructing parameters of spreading models from partial observations
Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities.
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.
Efficient reconstruction of transmission probabilities in a spreading process from partial observations
A number of recent papers introduced efficient algorithms for the estimation of spreading parameters, based on the maximization of the likelihood of observed cascades, assuming that the full information for all the nodes in the network is available.
