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Point Processes

30 papers with code · Methodology

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Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling

10 Jul 2017X-DataInitiative/tick

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.

POINT PROCESSES SURVIVAL ANALYSIS

DPPy: Sampling DPPs with Python

19 Sep 2018guilgautier/DPPy

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning.

POINT PROCESSES

Zonotope hit-and-run for efficient sampling from projection DPPs

ICML 2017 guilgautier/DPPy

Previous theoretical results yield a fast mixing time of our chain when targeting a distribution that is close to a projection DPP, but not a DPP in general.

POINT PROCESSES RECOMMENDATION SYSTEMS

Determinantal point processes for machine learning

25 Jul 2012guilgautier/DPPy

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.

POINT PROCESSES

PoPPy: A Point Process Toolbox Based on PyTorch

23 Oct 2018HongtengXu/PoPPy

In practice, the key points of point process-based sequential data modeling include: 1) How to design intensity functions to describe the mechanism behind observed data?

POINT PROCESSES

Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations

19 May 2019microsoft/DiCE

Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.

POINT PROCESSES

THAP: A Matlab Toolkit for Learning with Hawkes Processes

28 Aug 2017HongtengXu/Hawkes-Process-Toolkit

As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.

POINT PROCESSES

Learning Hawkes Processes from Short Doubly-Censored Event Sequences

ICML 2017 HongtengXu/Hawkes-Process-Toolkit

Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.

POINT PROCESSES

A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

NeurIPS 2017 HongtengXu/Hawkes-Process-Toolkit

We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.

BAYESIAN INFERENCE POINT PROCESSES

Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

11 Apr 2016catniplab/vLGP

In the V1 dataset, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation.

POINT PROCESSES