Computer Science Doctoral Dissertations

Computer Science Doctoral Dissertations-20
Milroy PDF Techniques to Leverage RF Signals for Context Sensing, Phuc Van Nguyen PDF Design and Empirical Evaluation of Interactive and Interpretable Machine Learning, Forough Poursabzi-Sangdeh PDF Scalable and Timely Detection of Cyberbullying in Online Social Networks, Rahat Ibn Rafiq PDF Inductive Certificate Synthesis for Control Design, Hadi Ravanbakhsh PDF Content-Style Decomposition: Representation Discovery and Applications, Karl F.Ridgeway PDF Measuring the Role of Visualization on Missing Values in Time Series Data, Hayeong Song PDF Analyzing Posterior Variability in Topic Models, Linzi Xing PDF Automatic Scaling of Cloud Applications via Transparently Elasticizing Virtual Memory, Ehab N.

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Ababneh PDF Model Identification and Control of Autonomous Ground Vehicles, Sina Aghli PDF Dynamic Active Subspaces: a Data-Driven Approach to Computing Time-Dependent Active Subspaces in Dynamical Systems, Izabel Pirimai Aguiar PDF Parallel Bundle Adjustment of High Resolution Satellite Imagery, James Balasalle PDF Ratchet Mechanisms in Macroevolutionary Processes, Trevor J.

In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.

Ma is an Assistant Professor of Computer Science and Statistics at Stanford University.

However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures.

In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures.

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