Point Processes
98 papers with code • 0 benchmarks • 2 datasets
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Most implemented papers
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
microsoft/DiCE
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Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions.
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
Kronecker Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items.
Determinantal thinning of point processes with network learning applications
A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques.
Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.
Modelling Behavioural Diversity for Learning in Open-Ended Games
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e. g., Rock-Paper-Scissors).
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
nobug-code/Diversity_Networks_CNN_Pytorch
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We introduce Divnet, a flexible technique for learning networks with diverse neurons.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
xiaoshuai09/recurrent-point-process
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In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
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.
DPPy: Sampling DPPs with Python
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.
