Dimitra Maoutsa's Projects
General-purpose dimensionality reduction and manifold learning tool based on Variational Autoencoder, implemented in TensorFlow.
Model-free method for inferring synaptic interactions from spike train recordings.
Repository for Deterministic Particle Flow Control framework
Trial website
Perlin noise based flow agents made in Unity 3D.
Repository for inference method for stochastic processes through geometric path augmentation
Geometric loss functions between point clouds, images and volumes
Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learning
Topological analysis of brain activity data
Lecture material for part (4/12) of the lecture **Network Dynamics & Complex Systems - Theoretical and Computational Tools** given during winter semester 2016/17 at Georg-August-Universität Göttingen
The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)
Blog of M Dims >>> https://dimitra-maoutsa.github.io/M-Dims-Blog/
Comprehensive list of resources on the topic of digital morphogenesis (the creation of form through code). Includes links to major articles, code repos, creative projects, books, software, and more.
Attempt at implementing system described in "Neural Turing Machines." by Graves, Alex, Greg Wayne, and Ivo Danihelka. (http://arxiv.org/abs/1410.5401)
The framework for inferring Langevin dynamics from spike data
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Playground repo for solving differential equations using the neurodiffeq package
Deterministic particle dynamics for simulating Fokker-Planck probability flows
old code
Physics-Informed Neural networks for Advanced modeling
Detailed explanation of the Deterministic Particle Control method briefly outlined in [upcoming paper] + code
A Python implementation of the matching pursuit sparse coding algorithm.
A highly optimized, parallel implementation of the Batch-OMP version of the KSVD learning algorithm.
Visualisations of data are at the core of every publication of scientific research results. They have to be as clear as possible to facilitate the communication of research. As data can have different formats and shapes, the visualisations often have to be adapted to reflect the data as well as possible. We developed Pylustrator, an interface to directly edit python generated matplotlib graphs to finalize them for publication. Therefore, subplots can be resized and dragged around by the mouse, text and annotations can be added. The changes can be saved to the initial plot file as python code.
Orthogonal Matching Pursuit (Python)
A framework for Smoothed Particle Hydrodynamics in Python