In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect ...In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network.The technique can help answer questions,such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output.Our visual analytics approach comprises several components:First,our input visualization shows the input sequence and how it relates to the output(using color coding).In addition,hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states.Trajectories are also employed to show the details of the evolution of the hidden state configurations.Finally,a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers,and a histogram indicates the distances between the hidden states within the original space.The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences.To demonstrate the capability of our approach,we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.展开更多
Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system ...Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.展开更多
In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Develo...In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over;seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.展开更多
The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilist...The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.展开更多
不确定性和隐状态是目前强化学习所要面对的重要难题.本文提出了一种新的算法MA-Q-learning算法来求解带有这种不确定性的POMDP问题近似最优策略.利用M em etic算法来进化策略,而Q学习算法得到预测奖励来指出进化策略的适应度值.针对隐...不确定性和隐状态是目前强化学习所要面对的重要难题.本文提出了一种新的算法MA-Q-learning算法来求解带有这种不确定性的POMDP问题近似最优策略.利用M em etic算法来进化策略,而Q学习算法得到预测奖励来指出进化策略的适应度值.针对隐状态问题,通过记忆agent最近经历的确定性的有限步历史信息,与表示所有可能状态上的概率分布的信度状态相结合,共同决策当前的最优策略.利用一种混合搜索方法来提高搜索效率,其中调整因子被用于保持种群的多样性,并且指导组合式交叉操作与变异操作.在POMDP的Benchm ark实例上的实验结果证明本文提出的算法性能优于其他的POMDP近似算法.展开更多
基金Funded by the Deutsche Forschungsgemeinschaft(German Research Foundation),No.251654672—TRR 161(Project B01)Germany’s Excellence Strategy,No.EXC-2075—390740016.
文摘In this paper,we introduce a visual analytics approach aimed at helping machine learning experts analyze the hidden states of layers in recurrent neural networks.Our technique allows the user to interactively inspect how hidden states store and process information throughout the feeding of an input sequence into the network.The technique can help answer questions,such as which parts of the input data have a higher impact on the prediction and how the model correlates each hidden state configuration with a certain output.Our visual analytics approach comprises several components:First,our input visualization shows the input sequence and how it relates to the output(using color coding).In addition,hidden states are visualized through a nonlinear projection into a 2-D visualization space using t-distributed stochastic neighbor embedding to understand the shape of the space of the hidden states.Trajectories are also employed to show the details of the evolution of the hidden state configurations.Finally,a time-multi-class heatmap matrix visualizes the evolution of the expected predictions for multi-class classifiers,and a histogram indicates the distances between the hidden states within the original space.The different visualizations are shown simultaneously in multiple views and support brushing-and-linking to facilitate the analysis of the classifications and debugging for misclassified input sequences.To demonstrate the capability of our approach,we discuss two typical use cases for long short-term memory models applied to two widely used natural language processing datasets.
基金supported by the National Key R&D Program of China(2018YFB1306100)the National Natural Science Foundation of China(61922089,61833016,62073336,61903376,61773386)the National Science Foundation of Shannxi Province(2020JQ-489,2020JM-360).
文摘Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.
文摘In this paper, we will illustrate the use and power of Hidden Markov models in analyzing multivariate data over time. The data used in this study was obtained from the Organization for Economic Co-operation and Development (OECD. Stat database url: https://stats.oecd.org/) and encompassed monthly data on the employment rate of males and females in Canada and the United States (aged 15 years and over;seasonally adjusted from January 1995 to July 2018). Two different underlying patterns of trends in employment over the 23 years observation period were uncovered.
文摘The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.
文摘不确定性和隐状态是目前强化学习所要面对的重要难题.本文提出了一种新的算法MA-Q-learning算法来求解带有这种不确定性的POMDP问题近似最优策略.利用M em etic算法来进化策略,而Q学习算法得到预测奖励来指出进化策略的适应度值.针对隐状态问题,通过记忆agent最近经历的确定性的有限步历史信息,与表示所有可能状态上的概率分布的信度状态相结合,共同决策当前的最优策略.利用一种混合搜索方法来提高搜索效率,其中调整因子被用于保持种群的多样性,并且指导组合式交叉操作与变异操作.在POMDP的Benchm ark实例上的实验结果证明本文提出的算法性能优于其他的POMDP近似算法.
