Global ETD Search

871	Learning multi-agent pursuit of a moving target Lu, Jieshan Unknown Date No description available. moving target search features machine learning
872	Artificial intelligence in electrical machine condition monitoring Yang, Youliang Unknown Date No description available. artificial intelligence electrical machine condition monitoring
873	Learning Accurate Regressors for Predicting Survival Times of Individual Cancer Patients Lin, Hsiu-Chin Unknown Date No description available. Survival Prediction Medical Informatics Machine Learning
874	Assisting Failure Diagnosis through Filesystem Instrumentation Huang, Liang Unknown Date No description available. Software maintenance Failure diagnosis Machine learning
875	USING SNP DATA TO PREDICT RADIATION TOXICITY FOR PROSTATE CANCER PATIENTS Mirzazadeh, Farzaneh Unknown Date No description available.
876	Probe-Efficient Learning Zolghadr, Navid Unknown Date No description available. Machine Learning Online Learning Costly Observations
877	A general framework for reducing variance in agent evaluation White, Martha Unknown Date No description available. variance reduction machine learning agent evaluation
878	A mathematical approach to the abstract synthesis of sequential discrete systems. Jerome, Emile Julien January 1970 (has links) No description available. Sequential machine theory Discrete-time systems
879	Apprentissage quantique Gambs, Sébastien January 2008 (has links) Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal Apprentissage quantique Informatique quantique Apprentissage machine
880	Modelling motor cortex using neural network control laws Lillicrap, Timothy Paul 31 January 2014 (has links) The ease with which our brains learn to control our bodies belies intricate neural processing which remains poorly understood. We know that a network of brain regions work together in a carefully coordinated fashion to allow us to move from one place to another. In mammals, we know that the motor cortex plays a central role in this process, but precisely how its activity contributes to control is a matter of long and continued debate. In this thesis we demonstrate the need for developing mechanistic neural network models to address this question. Using such models, we show that contentious response properties of non-human primate primary motor cortex (M1) neurons can be understood as reflecting control processes which take into account the physics of the body. And we develop new computational techniques for teaching neural network models how to execute control. In the first study (Chapter 2), we critically examined a recently developed correlation-based descriptive model for characterizing the activity of M1 neuron activity. In the second study (Chapter 3), we developed neural network control laws which performed reaching and postural tasks using a physics model of the upper limb. We show that the population of artificial neurons in these networks exhibit preferences for certain directions of movement and certain forces applied during posture. These patterns parallel empirical observations in M1, and the model shows that the patterns reflect particular features of the biomechanics of the arm. The final study (Chapter 4) develops new techniques for building network models. To understand how the brain solves difficult control tasks we need to be able to construct mechanistic models which can do the same. And, we need to be able to construct controllers that compute via simple neuron-like units. In this study, we combine tools for automatic computation of derivatives with recently developed ideas about second-order approaches to optimization to build better neural network control laws. Taken together, this thesis helps develop arguments for, and the tools to build mechanistic neural network models to understand how motor cortex contributes to control of the body. / Thesis (Ph.D, Neuroscience) -- Queen's University, 2014-01-31 10:34:43.816 Motor Cortex Machine Learning Neural Networks Neuroscience

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