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 8 of 8 for “"Non-identifiability"”.
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Towards Better Representations with Deep/Bayesian Learning
… models are unified. It further raises the non-identifiability issues in bidirectional adversarial learning, and propose ALICE algorithms: a conditional entropy framework to remedy the issues. The derived algorithms show significant improvement in the tasks of image generation and …
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Accuracy-aware privacy mechanisms for distributed computation
… privacy definition for distributed computation ""non-identifiability"", that allow us to simultaneously guarantee privacy and the accuracy of the computed solution. This definition involves showing that information observed by the adversary is compatible with several distributed computing problems …
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A quantitative framework For large-scale model estimation and discrimination In systems biology
… parameter distributions is largely determined by non-identifiability but co-variation among parameters, even those that are poorly determined, encodes essential information. Knowledge of joint parameter distributions makes it possible to compute the uncertainty of model-based predictions whereas …
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Development of constrained fuzzy logic for modeling biological regulatory networks and predicting contextual therapeutic effects
… equally well, and it is crucial to consider this non-identifiability during model training and subsequence analysis. Our trained models generate new biological understanding of network crosstalk as well as quantitative predictions of signaling protein activation. In our next applications of cFL, …
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Exploring the network’s world: From omics-driven machine learning workflow for drug target identification to quantification of signaling model diversity.
… networks from omics data poses challenges due to non-identifiability, resulting in multiple valid solutions consistent with the data. After that, the focus shifts towards quantifying signaling model diversity through solver-agnostic solution sampling with CORNETO, an ongoing effort that aims to …
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LONGITUDINAL SOLUTE TRANSPORT IN OPEN-CHANNEL FLOW - A Numerical Simulation Study on Longitudinal Dispersion, Surface Storage Effects, Transverse Mixing, Uncertainties and Parameter-Transferring Problems
… (parameterization) method is challenged by the non-identifiability which is common to all inverse modeling, and it seems TSM cannot be easily used as a predictive tool, more of an interpretive tool of solute transport, i.e., is the parameter set calibrated via inverse modeling transferable? …
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Statistical Models for Gene and Transcripts Quantification and Identification Using RNA-Seq Technology
… some other cases, and may even suffer from the non-identifiability problem. A key drawback of these existing methods is that they fail to utilize all the formation in the RNA-Seq short read count data. In this thesis, we propose three model frameworks to address three important questions in …
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Interpreting Deep Neural Networks and Beyond: Visualization, Learning Dynamics, and Disentanglement
… to train and the reasons underlying their (non-)convergence behaviors are still not completely understood. To this end, we conduct a non-asymptotic analysis of local convergence in GAN training dynamics by evaluating the eigenvalues of its Jacobian near the equilibrium. The analysis reveals …