Point-Processes
dpmmpythonStreaming
| Point-Processes | dpmmpythonStreaming | |
|---|---|---|
| 2 | 3 | |
| 37 | 14 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Point-Processes
-
[R] DeepDPM: Deep Clustering With an Unknown Number of Clusters
https://github.com/VincentGranville/Point-Processes/commit/2c2ed7cc989711d0a40d96fb6f194c690fcded8f (left is original data)
-
What is the hardest thing to learn in statistics?
As for confidence regions, you show them the a plot like this one. Each contour line defines a confidence region of a certain level (that can be determined accurately). This stuff is familiar to hikers using a map to navigate the terrain. No math involved in the whole teaching experience, other than stuff from elementary school.
dpmmpythonStreaming
-
[P] Looking for state of the art clustering algorithms
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
-
[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Code (Python wrapper): https://github.com/BGU-CS-VIL/dpmmpythonStreaming
-
[R] DeepDPM: Deep Clustering With an Unknown Number of Clusters
We have yet to publish a streaming-data solution, but it is definitely a direction we are considering. That said, there is nothing refraining you from keep training the network. However, one should carefully design how you update the input (training) data. There is also this solution that seems interesting: https://github.com/BGU-CS-VIL/dpmmpythonStreaming
What are some alternatives?
GPflow - Gaussian processes in TensorFlow
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
DPMMSubClusters_GPU - DPMM Sub Clusters C++ on GPU Cross Platforms (Windows & Linux)
Stochastic-Processes - My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.
cuml - cuML - RAPIDS Machine Learning Library
DPMMSubClustersStreaming.jl - Code for our AISTATS '22 paper "Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data"
VersatileHDPMixtureModels.jl - Code for our UAI '20 paper "Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes"
dpmmpython - Python wrapper for the DPMMSubCluster Julia package for inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
pdc-dp-means - "Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]
DPMMSubClusters.jl - Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
cudf - cuDF - GPU DataFrame Library