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Found 11 papers, 7 papers with code
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data.
Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure.
Implications of sparsity and high triangle density for graph representation learning
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inner products.
Graph Representation Learning
Vocal Bursts Intensity Prediction
Statistical exploration of the Manifold Hypothesis
The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space.
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
In contrast to the popular ODE approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency of maximum PAL estimators.
Matrix factorisation and the interpretation of geodesic distance
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions.
Inference in Stochastic Epidemic Models via Multinomial Approximations
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability.
Dynamic Bayesian Neural Networks
We define an evolving in time Bayesian neural network called a Hidden Markov neural network.
Dynamic time series clustering via volatility change-points
This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points.
Methodology Statistical Finance
Exploiting locality in high-dimensional factorial hidden Markov models
We propose algorithms for approximate filtering and smoothing in high-dimensional Factorial hidden Markov models.
The infinite Viterbi alignment and decay-convexity
Quantitative bounds on the distance to the infinite Viterbi alignment, which are the first of their kind, are derived and used to illustrate how approximate estimation via parallelization can be accurate and scaleable to high-dimensional problems because the rate of convergence to the infinite Viterbi alignment does not necessarily depend on $d$.
