Julia implementation of low-rank determinantal point process (DPP) learning and prediction algorithms.
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Updated
Jun 7, 2019 - Julia
Julia implementation of low-rank determinantal point process (DPP) learning and prediction algorithms.
Generator loss to reduce mode-collapse and to improve the generated samples quality.
Companion paper for DPPy
Simulates a random determinantally-thinned Poisson point process on a rectangle.
code for the paper "In Conclusion Not Repetition:Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes"
A project to implement Structured Determinantal Point Process algorithms.
Implementation of Learning Instance-Aware Object Detection Using Determinantal Point Processes, CVIU 2020
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" [PRICAI 2021].
Implementation of Bayesian experimental design using regularized determinantal point processes
Python toolbox for sampling Determinantal Point Processes
PyTorch implementation of nonsymmetric determinantal point process (DPP) learning.
[ICML 2023] Code for our paper “Compositional Exemplars for In-context Learning”.
Sub-package of spatstat containing functionality for parametric modelling and inference
Group Recommendation Systems with Diversity-based Clustering and Game Theory
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