- Help with understanding Wasserstein distance https://en.wikipedia.org/wiki/Wasserstein_metric 7 comments math
Linking pages
- Connections between SVMs, Wasserstein distance and GANs – Alexia Jolicoeur-Martineau https://ajolicoeur.wordpress.com/maximummargingans/ 1 comment
- What does it really mean for an algorithm to be biased? https://thegradient.pub/ai-bias/ 1 comment
- Markov Musings 1: The Fundamental Theorem – Ethan Epperly https://www.ethanepperly.com/index.php/2023/06/29/markov-musings-1-the-fundamental-theorem/ 1 comment
- Estimating a cumulative distribution function with differential privacy | by Clément Gastaud | Sarus Blog | Medium https://medium.com/sarus/estimating-a-cumulative-distribution-function-with-differential-privacy-54433fab45c7 0 comments
- Robust Statistical Distances for Machine Learning | Datadog https://www.datadoghq.com/blog/engineering/robust-statistical-distances-for-machine-learning/ 0 comments
- GAN Objective Functions: GANs and Their Variations | by Hunter Heidenreich | Towards Data Science https://towardsdatascience.com/gan-objective-functions-gans-and-their-variations-ad77340bce3c 0 comments
- Generative Adversarial Networks (GANs), Some Open Questions – Off the convex path http://www.offconvex.org/2017/03/15/GANs/ 0 comments
- Read-through: Wasserstein GAN http://www.alexirpan.com/2017/02/22/wasserstein-gan.html 0 comments
- Imitation Learning in the Low-Data Regime – Google AI Blog https://ai.googleblog.com/2020/09/imitation-learning-in-low-data-regime.html 0 comments
- Distributional Bellman and the C51 Algorithm | Felix Yu https://flyyufelix.github.io/2017/10/24/distributional-bellman.html 0 comments
- Using Variational Transformer Networks to Automate Document Layout Design – Google AI Blog https://ai.googleblog.com/2021/06/using-variational-transformer-networks.html 0 comments
- GitHub - higgsfield/RL-Adventure: Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL https://github.com/higgsfield/RL-Adventure 0 comments
- Distribution visualizations for data science | by Milo | spikelab | Medium https://medium.com/spikelab/distribution-visualizations-for-data-science-bb8da084a9b6 0 comments
- Fairness and discrimination, PhD Course, #4 Wasserstein Distances and Optimal Transport | R-bloggers https://www.r-bloggers.com/2024/01/fairness-and-discrimination-phd-course-4-wasserstein-distances-and-optimal-transport/ 0 comments
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