Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 317 for “"non-stationary"”.
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Learning in non-stationary Environments
… domain is assumed to be the same, denoted as a stationary environment. If this is not the case and the distributions change between the two domains, it is called a non-stationary environment. <br /><br /> The research area of Domain Adaptation offers methods to adapt the input data or an already …
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Online learning of non-stationary sequences
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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Variational inference for non-stationary distributions
… and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question.
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Non-stationary Vehicle-to-Vehicle Channel Characterization
… environment, the channel may be statistically non-stationary (NS), and traditional wide-sense stationary uncorrelated scattering (WSSUS) channel models will only be applicable for short durations. In this dissertation, we propose and evaluate NS V2V channel models in the 5 GHz band. We present …
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Effective Learning in Non-Stationary Multiagent Environments
… simultaneously learn in MARL, leading to natural non-stationarity in the experiences encountered and thus requiring each agent to its behavior with respect to potentially large changes in other agents' policies. This thesis aims to address the non-stationarity challenge in multiagent learning from …
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Stationary Subspace Analysis: Towards understanding non-stationary data
… Entwicklung des ersten unüberwachten Verfahrens, Stationary Subspace Analysis (SSA), welches eine lineare Koordinatentransformation findet, die die beobachteten Daten in eine Gruppe von stationären und nicht-stationären Komponenten faktorisiert. Dies ist unerlässlich zur Analyse multivariater …
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Causal Inference: Heterogeneous Effects and Non-stationary Environments
… the problem of estimating treatment effects in non-stationary data. In this setting, using old data to make inferences can lead to unreliable results. We propose a novel procedure that helps smooth out the data non-stationarity by providing a way to resample previous data to match the …
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Statistical models for dependent and non-stationary extreme events
… propose a method for modelling the extremes of a non-stationary univariate process; we then extend this methodology to model a multivariate process with non-stationary marginal and dependence structures. Finally we consider a new estimator for the dependence structure of a sequence of multivariate …
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Optimized supply routing at Dell under non-stationary demand
This thesis describes the design and implementation of an optimization model to manage inventory at Dell's American factories. Specifically, the model is a mixed integer program which makes routing decisions on incoming monitors (a bulky item which incurs great shipping costs) from Asia to Dell's …
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Learning non-stationary SVBRDFs using GANs and differentiable rendering
In this thesis we propose a learning approach for generating realistic SVBRDFs using generative adversarial models and differentiable rendering. Our model learns a mapping from the geometry buffer of a surface to a corresponding albedo texture-map by training on images of the same surface rendered …
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IMAGE-BASED MODELING AND PREDICTION OF NON-STATIONARY GROUND MOTIONS
Nonlinear dynamic analysis is a required step in seismic performance evaluation of many structures. Performing such an analysis requires input ground motions, which are often obtained through simulations, due to the lack of sufficient records representing a given scenario. As seismic ground motions …
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Developing Learning Methods for Non-stationary and Imbalanced Data Streams
… learning tasks. Despite "learning from non-stationary streams" and "class imbalance" problems having been investigated separately in the literature, too little attention has been paid to the multi-class imbalance problem as it can emerge in evolving streams. This thesis is devoted to the …
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Model-free reinforcement learning in non-stationary Markov Decision Processes
… functions of the MDP are time-invariant. Such a stationary model, however, cannot capture the dynamic nature of many sequential decision-making problems in practice. In this thesis, we consider the problem of reinforcement learning in \emph{non-stationary} MDPs. In our setting, both the reward …
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Learning with high dimensional data and preprocessing in non-stationary environments
… the Random Projection technique is analyzed in non-stationary envi- ronments. It is shown, that the Johnson-Lindenstrauss Lemma also holds for stream classification tasks. Further, performance comparisons of different classifiers on the projected and the orig- inal space are provided, and it is …
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A NEW ZEROTH-ORDER ORACLE FOR DISTRIBUTED AND NON-STATIONARY LEARNING
… residual feedback scheme for both convex and nonconvex online optimization problems. Specifically, for both deterministic and stochastic problems and for both Lipschitz and smooth objective functions, we show that using residual feedback can produce gradient estimates with much smaller …
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