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Maximum Density Divergence for Domain Adaptation, TPAMI 2020, Code release, Cross-domain Adversarial Tight Match
Performs Bayesian conditional density estimation using a tree structure and logistic Gaussian processes.
The Brain Connectivity Toolbox: https://sites.google.com/site/bctnet/
brain connectivity toolbox for python
Generalzied divergence-guided multi-view learning
All about domain generalization
DomainBed is a suite to test domain generalization algorithms
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
https://github.com/1Konny/FGSM.git
Use Resnet to preprocess the image data and then embedding the feature using much shallow networks to handle complex data. If data has been preprocessed, load the extracted features directly.
The generalized Jensen-Renyi's divergence (GJRD), serving as a robust tool for estimating the divergence between multiple distributions, is introduced to handle machine learning problems involving data from multiple distributions or sources. This work specifies GJRD-based deep clustering as a case study.
Fast spike sorting with drift correction for up to a thousand channels
Config files for my GitHub profile.
Domain Generalization with MixStyle (ICLR'21)
Code for Multi-Source Domain Adaptation with data pre-processed with resnet.
Post-hoc Nemenyi test for algorithm statistical comparison.
Implementation of different Normalizing Flows, NF, Planar Flows, IAF, etc.
PyTorch implementation of DEC (Deep Embedding Clustering)
Kolmogorov Arnold Networks
Real-valued non-volume preserving(RealNVP) implementation with PyTorch
滤波器设计之路(The road to filter-design, including FIR, IIR, sinc, Butterworth, etc.)
Python implementation of signal processing techniques and K-means clustering to sort spikes.
PyTorch implementation of "Learning Stable Deep Dynamics Models" (https://papers.nips.cc/paper/9292-learning-stable-deep-dynamics-models), with extensions to controlled dynamical systems.
Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)
A fast and unsupervised algorithm for spike detection and sorting using wavelets and super-paramagnetic clustering
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.