From 62ff4c81bf64b6dc38054f5babfc4524ff94ef47 Mon Sep 17 00:00:00 2001 From: bxdd Date: Tue, 25 Apr 2023 14:41:07 +0800 Subject: [PATCH] [DOC] Update docs --- docs/components/market.rst | 9 +++------ docs/components/spec.rst | 12 ++++++++++-- docs/index.rst | 2 +- 3 files changed, 14 insertions(+), 9 deletions(-) diff --git a/docs/components/market.rst b/docs/components/market.rst index 18a80cb..89fc5ad 100644 --- a/docs/components/market.rst +++ b/docs/components/market.rst @@ -13,7 +13,7 @@ In the learnware paradigm, there are three key players: *developers*, *users*, a This process can be broken down into two main stages. Submitting Stage ---------------------- +====================================== During the *submitting stage*, developers can voluntarily submit their trained models to the learnware market. The market will then implement a quality assurance mechanism, such as performance validation, to determine if a submitted model is suitable for acceptance. In a learnware market with millions of models, identifying potentially helpful models for a new user is a challenge. @@ -23,8 +23,8 @@ The solution's crux lies in the *specification*, which is central to the learnwa In a learnware market, heterogeneous models may have different :math:`\mathcal{X}`, :math:`\mathcal{Y}`, or objectives. If we refer to the specification space that covers all possible models in all possible functional spaces as the 'specification world' analogously, then each specification space corresponding to one possible functional space can be called a 'specification island.' Designing an elegant specification format that encompasses the entire specification world and allows all possible models to be efficiently and adequately identified is a significant challenge. Currently, we adopt a practical design, where each learnware's specification consists of two parts. -====================================== -Learnware Market and Specifications + +Reusing Stage ====================================== Creating Learnware Specifications @@ -50,6 +50,3 @@ Reusing Learnwares Once helpful learnwares are identified and delivered to the user, they can be reused in various ways. Users can apply the received learnware directly to their data, use multiple learnwares to create an ensemble, or adapt and polish the received learnware(s) using their own data. Learnwares can also be used as feature augmentors, with their outputs used as augmented features for building the final model. Helpful learnwares may be trained for tasks that are not exactly the same as the user's current task. In such cases, users can tackle their tasks in a divide-and-conquer way or reuse the learnwares collectively through measuring the utility of each model on each testing instance. If users find it difficult to express their requirements accurately, they can adapt and polish the received learnwares directly using their own data. - - - diff --git a/docs/components/spec.rst b/docs/components/spec.rst index 946e871..97827d0 100644 --- a/docs/components/spec.rst +++ b/docs/components/spec.rst @@ -3,8 +3,10 @@ Specification ================================ -Learnware Specification ------------------------------ +The search of helpful learnwares can be divided into two stages: statistical specification and semantic specification. + +Statistical Specification +--------------------------- The learnware specification should ideally provide essential information about every model in the learnware market, enabling efficient and accurate identification for future users. Our current specification design has two components. The first part consists of a string of descriptions or tags assigned by the learnware market based on developer-submitted information. These descriptions or tags help identify the model's specification island. Different learnware market enterprises may use different descriptions or tags. @@ -57,3 +59,9 @@ a more general result has been achieved: where :math:`R(g)=\sum_{i=1}^N w_i \mathbb{E}_{\mathcal{D}_1}\left[\ell_{01}(g(\boldsymbol{x}), i)\right]` represents the weighted risk of any learnware selector :math:`g(x)`, which takes unlabeled data as input and assigns it to the appropriate model, :math:`f(\boldsymbol{x})=\widehat{f}_{g(\boldsymbol{x})}(\boldsymbol{x})` is the final model for the user's task. Efforts have been made to enable the learnware market to handle unseen tasks, where the user's task involves some unseen aspects that have never been addressed by the current learnwares in the market. A more general theoretical analysis has been presented based on mixture proportion estimation. + + +Semantic Specification +--------------------------- + +The semantic specification describes the characteristics of user's task and the market will identify potentially helpful leaarnwares whose models solve tasks similar to your requirements. The detail semantic specification is in `Indentification Learnwares <../workflow/identify.html>`_. \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 48cf99e..1b6afcf 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -30,7 +30,7 @@ Document Structure .. toctree:: :maxdepth: 3 - :caption: WORKFLOWS: + :caption: MAIN WORKFLOWS: Learnware Preparation and Submission Helpful Learnwares Identification