From 45dd3c896e2164aa939299d331ebdabf0f3c87c6 Mon Sep 17 00:00:00 2001 From: godbaiqi Date: Tue, 18 Aug 2020 16:45:31 +0800 Subject: [PATCH] modify format bugs --- working-groups/research/Topic10_AutoML.md | 11 +++++------ .../Topic1_Low-bit-Neural-Networks-Training.md | 10 +++++----- .../research/Topic2_Memory-Optimization.md | 6 +++--- .../research/Topic3_Model-Innovation.md | 16 ++++++++-------- .../Topic4_AI-for-Scientific-Computing.md | 8 ++++---- .../research/Topic5_Verifiable-Trustworthy-AI.md | 10 +++++----- .../research/Topic6_Confidential-AI-Computing.md | 12 ++++++------ ...ensor-Differentiable-Calculation-Framework.md | 10 +++++----- ...ibuted-and-Parallel-AI-Computing-Framework.md | 6 +++--- working-groups/research/Topic9_Explainable-AI.md | 13 ++++++------- 10 files changed, 50 insertions(+), 52 deletions(-) diff --git a/working-groups/research/Topic10_AutoML.md b/working-groups/research/Topic10_AutoML.md index dc5931c..31e066c 100644 --- a/working-groups/research/Topic10_AutoML.md +++ b/working-groups/research/Topic10_AutoML.md @@ -2,13 +2,12 @@ ## Motivation： -​ Nowadays, training a model that meets the accuracy requirements often requires rich expert knowledge and repeated iterative attempts. Although there is AutoML technology, there are still problems of difficult search space setting and long training time for large search spaces. If you can combine the iterative history of user training and analyze historical data, a lightweight hyperparameter recommendation method can be realized, which can greatly improve the developer experience. - -​ Similarly, for performance tuning, there are similar problems. In different heterogeneous hardware, models, and data processing scenarios, expert knowledge is required for tuning. Therefore, we aim to reduce the performance tuning threshold by automatically identifying system performance bottlenecks and recommending the best code path. +​ Nowadays, training a model that meets the accuracy requirements often requires rich expert knowledge and repeated iterative attempts. Although there is AutoML technology, there are still problems of difficult search space setting and long training time for large search spaces. If you can combine the iterative history of user training and analyze historical data, a lightweight hyperparameter recommendation method can be realized, which can greatly improve the developer experience. +Similarly, for performance tuning, there are similar problems. In different heterogeneous hardware, models, and data processing scenarios, expert knowledge is required for tuning. Therefore, we aim to reduce the performance tuning threshold by automatically identifying system performance bottlenecks and recommending the best code path. ## Target： -​ Reduce model development cost, set up thresholds through automatic hyper-parameter configuration and performance optimization paths, and improve model debugging and optimization efficiency. +​ Reduce model development cost, set up thresholds through automatic hyper-parameter configuration and performance optimization paths, and improve model debugging and optimization efficiency. ## Method： @@ -16,5 +15,5 @@ ## How To Join： -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic1_Low-bit-Neural-Networks-Training.md b/working-groups/research/Topic1_Low-bit-Neural-Networks-Training.md index 4d4c30e..12ac8a3 100644 --- a/working-groups/research/Topic1_Low-bit-Neural-Networks-Training.md +++ b/working-groups/research/Topic1_Low-bit-Neural-Networks-Training.md @@ -2,11 +2,11 @@ ## Motivation: -​ At present, mixed precision can automatically adjust the accuracy of fp16 and fp32 for the network to improve training performance and memory optimization. Because operators have different costs on different AI chips, all optimization strategies for different AI chips are different. The network configuration of different hardware is different, so how to automatically generate the precision adjustment strategy that adapts to various hardware, especially the low bit strategy has become a difficult problem. +​ At present, mixed precision can automatically adjust the accuracy of fp16 and fp32 for the network to improve training performance and memory optimization. Because operators have different costs on different AI chips, all optimization strategies for different AI chips are different. The network configuration of different hardware is different, so how to automatically generate the precision adjustment strategy that adapts to various hardware, especially the low bit strategy has become a difficult problem. ## Target: -​ Self-adaptively provides a low-bit precision training mechanism for various networks. +​ Self-adaptively provides a low-bit precision training mechanism for various networks. ![target](target.PNG) @@ -14,7 +14,7 @@ ​ We expect the applicant can conduct low-bit neural networks training research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic2_Memory-Optimization.md b/working-groups/research/Topic2_Memory-Optimization.md index 074cb61..fc3750d 100644 --- a/working-groups/research/Topic2_Memory-Optimization.md +++ b/working-groups/research/Topic2_Memory-Optimization.md @@ -16,7 +16,7 @@ ​ We expect the applicant can conduct memory optimization research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic3_Model-Innovation.md b/working-groups/research/Topic3_Model-Innovation.md index fa59dc0..b27123e 100644 --- a/working-groups/research/Topic3_Model-Innovation.md +++ b/working-groups/research/Topic3_Model-Innovation.md @@ -2,15 +2,15 @@ ## Motivation： -1. In-depth probability model innovation: through the combination of neural network and probability model, the model can better help decision-making. -2. Graph neural network: The neural network is combined with the traditional graph structure, oriented to cognitive reasoning and future trends. -3. Model innovation combining traditional models and neural networks is a research hotspot. +* In-depth probability model innovation: through the combination of neural network and probability model, the model can better help decision-making. +* Graph neural network: The neural network is combined with the traditional graph structure, oriented to cognitive reasoning and future trends. +* Model innovation combining traditional models and neural networks is a research hotspot. ## Target： -- Complete probability sampling library and probability inference (learning the probability distribution of the overall sample through known samples) algorithm library -- Design new algorithms for dynamically changing heterogeneous graphs (different feature dimensions and different information aggregation methods) -- Trillion distributed graph data storage, segmentation and sampling +* Complete probability sampling library and probability inference (learning the probability distribution of the overall sample through known samples) algorithm library +* Design new algorithms for dynamically changing heterogeneous graphs (different feature dimensions and different information aggregation methods) +* Trillion distributed graph data storage, segmentation and sampling ## Method： @@ -18,5 +18,5 @@ ## How To Join： -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic4_AI-for-Scientific-Computing.md b/working-groups/research/Topic4_AI-for-Scientific-Computing.md index 337573e..66763c5 100644 --- a/working-groups/research/Topic4_AI-for-Scientific-Computing.md +++ b/working-groups/research/Topic4_AI-for-Scientific-Computing.md @@ -7,8 +7,8 @@ ## Target： - * AI modeling：Construct a neural network, training data and Loss function for scientific computing problems. - * AI solution：AI model solves differential equations, solves optimization problems, achieve the goal that the amount of high-order automatic differential calculation increases linearly with the order. +* AI modeling：Construct a neural network, training data and Loss function for scientific computing problems. +* AI solution：AI model solves differential equations, solves optimization problems, achieve the goal that the amount of high-order automatic differential calculation increases linearly with the order. ## Method： @@ -16,5 +16,5 @@ ## How To Join： -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic5_Verifiable-Trustworthy-AI.md b/working-groups/research/Topic5_Verifiable-Trustworthy-AI.md index 00056f5..bf5d33e 100644 --- a/working-groups/research/Topic5_Verifiable-Trustworthy-AI.md +++ b/working-groups/research/Topic5_Verifiable-Trustworthy-AI.md @@ -2,8 +2,8 @@ ## Motivation: -- Many aspects of trustworthy AI (or responsible AI), such as robustness, backdoor-free, fairness, privacy protection capabilities, and accountability, have gradually attracted the attention of the industry and academia. -- Scholars' understanding and research on the attributes of trustworthy AI are mostly empirical, and there are few theoretical studies. The verifiable and certifiable analysis, tuning, and evaluation methods of trustworthy AI attributes and bounds, and their relation to explainable AI, require theoretical guidance. +* Many aspects of trustworthy AI (or responsible AI), such as robustness, backdoor-free, fairness, privacy protection capabilities, and accountability, have gradually attracted the attention of the industry and academia. +* Scholars' understanding and research on the attributes of trustworthy AI are mostly empirical, and there are few theoretical studies. The verifiable and certifiable analysis, tuning, and evaluation methods of trustworthy AI attributes and bounds, and their relation to explainable AI, require theoretical guidance. ## Target: @@ -13,7 +13,7 @@ ​ We expect the applicant can conduct Verifiable Trustworthy AI research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com diff --git a/working-groups/research/Topic6_Confidential-AI-Computing.md b/working-groups/research/Topic6_Confidential-AI-Computing.md index e8df265..2b36c4c 100644 --- a/working-groups/research/Topic6_Confidential-AI-Computing.md +++ b/working-groups/research/Topic6_Confidential-AI-Computing.md @@ -2,9 +2,9 @@ ## Motivation: -- In the training and deployment process of AI services, several vital resources such as data, models, and computing resources may belong to different parties, so a large amount of data will move across trust domains. The problems of data privacy protection and model confidentiality protection are prominent. -- Confidential computing is an important direction to protect the confidentiality of key data. At present, confidential computing based on trusted execution environment has performance advantages, but its trust model is limited; the trust model of confidential computing based on cryptography (homomorphic encryption, multi-party computing) is simple, but there is still a gap between performance and practicality. -- A series of specialized optimizations may improve the performance of confidential computing in AI scenarios, including but not limited to: cryptography suitable for AI, specialized intermediate representation and compling strategy for confidential AI computing, and hardware-based acceleration. +* In the training and deployment process of AI services, several vital resources such as data, models, and computing resources may belong to different parties, so a large amount of data will move across trust domains. The problems of data privacy protection and model confidentiality protection are prominent. +* Confidential computing is an important direction to protect the confidentiality of key data. At present, confidential computing based on trusted execution environment has performance advantages, but its trust model is limited; the trust model of confidential computing based on cryptography (homomorphic encryption, multi-party computing) is simple, but there is still a gap between performance and practicality. +* A series of specialized optimizations may improve the performance of confidential computing in AI scenarios, including but not limited to: cryptography suitable for AI, specialized intermediate representation and compling strategy for confidential AI computing, and hardware-based acceleration. ## Target: @@ -14,7 +14,7 @@ ​ We expect the applicant can conduct Confidential AI Computing research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic7_Tensor-Differentiable-Calculation-Framework.md b/working-groups/research/Topic7_Tensor-Differentiable-Calculation-Framework.md index 47b9121..dfd9410 100644 --- a/working-groups/research/Topic7_Tensor-Differentiable-Calculation-Framework.md +++ b/working-groups/research/Topic7_Tensor-Differentiable-Calculation-Framework.md @@ -7,15 +7,15 @@ * The technical challenges of unified optimization of the model layer and the operator layer, including hierarchical IR design, optimization of infrastructure, automatic tuning, loop optimization, etc. * Differential equations are solved with a large number of differentials, which have high requirements for the differential expression of the framework, interface design, algorithm analysis efficiency and reliability. -##Target: +## Target: ​ Driven by cutting-edge applications, from the perspectives of new models, dynamic models, high-performance computing languages, etc., study the evolution direction and key technology paths of future computing frameworks. For example, it supports differentiable programming of high-order differentiation and is compatible with traditional Fortran/C numerical calculation framework. -##Method: +## Method: ​ We expect the applicant can conduct tensor differentiable calculation framework research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic8_Distributed-and-Parallel-AI-Computing-Framework.md b/working-groups/research/Topic8_Distributed-and-Parallel-AI-Computing-Framework.md index 2b7b213..da41333 100644 --- a/working-groups/research/Topic8_Distributed-and-Parallel-AI-Computing-Framework.md +++ b/working-groups/research/Topic8_Distributed-and-Parallel-AI-Computing-Framework.md @@ -14,7 +14,7 @@ ​ We expect the applicant can conduct distributed and parallel AI computing framework research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support. -## How To Join +## How To Join: -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file diff --git a/working-groups/research/Topic9_Explainable-AI.md b/working-groups/research/Topic9_Explainable-AI.md index cdc3c2f..cb879dc 100644 --- a/working-groups/research/Topic9_Explainable-AI.md +++ b/working-groups/research/Topic9_Explainable-AI.md @@ -1,12 +1,11 @@ # Topic9：Explainable AI -##Motivation： +## Motivation： -​ The current deep learning model is essentially black box due to its technical complexity , which leads to the opacity and inexplicability of AI services and further restricts the commercial application and promotion of AI services. Existing interpretable AI technology mainly focuses on how to provide limited engineering auxiliary information to the model, but ignores the understanding of AI models from the perspective of human cognition +​ The current deep learning model is essentially black box due to its technical complexity , which leads to the opacity and inexplicability of AI services and further restricts the commercial application and promotion of AI services. Existing interpretable AI technology mainly focuses on how to provide limited engineering auxiliary information to the model, but ignores the understanding of AI models from the perspective of human cognition +​Humans usually understand things through analogies, metaphors, induction and other cognitive methods, and have a certain process of mental cognition construction. Thus, in this project, we expect to be able to explore more systematic and interpretable AI methods that conform to human cognition, including interactive interfaces, interpretation methods, measurement methods, and so on. -​ Humans usually understand things through analogies, metaphors, induction and other cognitive methods, and have a certain process of mental cognition construction. Thus, in this project, we expect to be able to explore more systematic and interpretable AI methods that conform to human cognition, including interactive interfaces, interpretation methods, measurement methods, and so on. - -##Target： +## Target： ​ A complete set of interpretable AI methods and strategies in line with human cognition, providing necessary interactive cognitive interface design solutions for different scenarios and different cognitions, and a case study for typical scenarios. @@ -16,5 +15,5 @@ ## How To Join： -1. Submit an issue/PR based on community discussion for consultation or claim on related topics -2. Submit your proposal to us by email xxx@huawei.com \ No newline at end of file +* Submit an issue/PR based on community discussion for consultation or claim on related topics +* Submit your proposal to us by email xxx@huawei.com \ No newline at end of file -- Gitee