Sub-package of spatstat containing functionality for parametric modelling and inference
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Updated
Jun 28, 2024 - R
Sub-package of spatstat containing functionality for parametric modelling and inference
[ICML 2023] Code for our paper “Compositional Exemplars for In-context Learning”.
PyTorch implementation of nonsymmetric determinantal point process (DPP) learning.
Python toolbox for sampling Determinantal Point Processes
Implementation of Bayesian experimental design using regularized determinantal point processes
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" [PRICAI 2021].
Implementation of Learning Instance-Aware Object Detection Using Determinantal Point Processes, CVIU 2020
A project to implement Structured Determinantal Point Process algorithms.
code for the paper "In Conclusion Not Repetition:Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes"
Simulates a random determinantally-thinned Poisson point process on a rectangle.
Companion paper for DPPy
Generator loss to reduce mode-collapse and to improve the generated samples quality.
Julia implementation of low-rank determinantal point process (DPP) learning and prediction algorithms.
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