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| Technical Roadmap for 2021 | |||
| ==== | |||
| Probabilistic Programming Layer | |||
| ---- | |||
| Distribution: | |||
| - [D1] add support to multivariate continous distributions | |||
| - [D1.1] MultivariateNormal | |||
| - [D1.2] GumbelSoftmax | |||
| - [D1.3] Dirichlet | |||
| Expected time: Q3 | |||
| Modeling Language: | |||
| - [M1] Based on the new support of MindSpore, extend the BayesianNet modeling capacity to general | |||
| - [M1.1] Implement with dictionaies | |||
| - [M1.2] Integrate StochasticTensor with Distribution | |||
| Expected time: Q1 | |||
| - [M2] Hybrid BayesianNet + Bijector modeling language | |||
| - [M2.1] Implement a bijector API, and integrate it with the existing bijector in distributions | |||
| - [M2.2] Implement affine coupling layer | |||
| - [M2.3] Reimplement the existing Glow / VFlow in the test folder with the new API | |||
| Depends on: M1 | |||
| Expected time: Q4 | |||
| Inference algorithm: | |||
| - [I1] Support common black-box algorithms | |||
| - [I1.1] Importance sampling | |||
| - [I1.2] SGLD | |||
| - [I1.3] Reinforce | |||
| - [I1.4] HMC | |||
| Depends on: M1 | |||
| Expected time: [I1.1]:Q1, [I1.2]:Q2, [I1.3]:Q3, [I1.4]:Q4 | |||
| Model and Toolbox Layer | |||
| ---- | |||
| - [T1] Reimplement existing model and toolbox with the probabilistic programming layer | |||
| - [T1.1] Reimplement VAE | |||
| - [T1.2] Reimplement BNN | |||
| - [T1.3] Reimplement Uncertainty Estimation | |||
| - [T1.4] Reimplement Anormaly Detection | |||
| Depends on: M1 | |||
| Expected time: Q2 | |||
| - [T2] Implement more SOTA algorithms of toolbox | |||
| - [T2.1] BNN with SGLD for uncertainty estimation | |||
| Depends on: M1, I1.2 | |||
| Expected time: Q2 | |||
| - [T2.2] SWAG for uncertainty estimation | |||
| Depends on: D1.1, M1 | |||
| Expected time: Q3 | |||
| (Optional) Test uncertainty estimation on language modeling | |||
| Appliation | |||
| ---- | |||
| - [A1] Apply our tools to representative applications | |||
| Depends on: T2 | |||
| Expected time: Q4 | |||
| Miscellanous | |||
| ---- | |||
| Keep interacting with MindSpore to improve the coding style. | |||
| Expected Goal | |||
| ---- | |||
| Q1: | |||
| - A general BayesianNet framework, that supports modeling of arbitary Bayesian networks | |||
| Q2: | |||
| - Support representative inference algorithms in the general format | |||
| - Rewrite applications and examples with the new interface | |||
| Q3: | |||
| - Implement more distributions, inference algorithms | |||
| - Implement some SOTA algorithms for uncertainty estimation | |||
| - Should have a comparable version of TF ZhuSuan now | |||
| Q4: | |||
| - Modeling tools for flow bases | |||
| - Tentative applications of uncertainty estimation | |||