A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
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Updated
May 2, 2024 - Jupyter Notebook
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
The implementation of MGNNI: Multiscale Graph Neural Networks with Implicit Layers (NeurIPS 2022)
Code for the DEQ experiments of the ICLR 2022 spotlight "SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models"
Code for the bi-level experiments of the ICLR 2022 paper "SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models" (on branch shine)
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
[ECE NTUA] 🎓 Diploma Thesis - Compressed Sensing MRI using Score-based Implicit Model (2022)
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