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11 - 20 of 38 search results for KaKaoTalk:vb20 200 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 38 match some words.

  1. Results that match 1 of 2 words

  2. thesis

    https://www.mlmi.eng.cam.ac.uk/files/burt_thesis.pdf
    6 Nov 2019: plotted for a synthetic data set with N = 200, x N(0,52) and s = 5. ... 1.2 that holds for large N plotted for a syntheticdata set with N = 200, x N(0,52) and s = 5.

  3. Investigating Inference in BayesianNeural Networks via Active…

    https://www.mlmi.eng.cam.ac.uk/files/riccardo_barbano_dissertation_mlmi.pdf
    18 Nov 2019: Initially, we train on200 labelled data-points, and progress in batches of 50 with a budget of 200. ... 200 epochs are used to guarantee convergence. 40. 7 A More Complex Dataset.

  4. Pathologies of Deep Sparse Gaussian Process Regression

    https://www.mlmi.eng.cam.ac.uk/files/diaz_thesis.pdf
    30 Oct 2019: Pathologies of Deep SparseGaussian Process Regression. Sergio Pascual Díaz. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Fitzwilliam College August 2017. Declaration. I,

  5. Overcoming Catastrophic Forgetting in Neural Machine Translation

    https://www.mlmi.eng.cam.ac.uk/files/kell_thesis.pdf
    6 Nov 2019: Overcoming Catastrophic Forgetting inNeural Machine Translation. Gregory Kell. Department of Engineering. University of Cambridge. This dissertation is submitted for the degree of. MPhil Machine Learning Speech and Language Technology. Wolfson

  6. Tradeoffs in Neural Variational Inference

    https://www.mlmi.eng.cam.ac.uk/files/cruz_dissertation.pdf
    30 Oct 2019: The celebA dataset ([39]) consists of more than 200,000 images of celebrity faces. ... For ourwork, we consider 200,000 of these which we split as follows:. •

  7. Improving Sample Efficiency forGradient-based Policy Optimisation;…

    https://www.mlmi.eng.cam.ac.uk/files/wang_dissertation.pdf
    30 Oct 2019: Improving Sample Efficiency forGradient-based Policy Optimisation;. with an Application to Structured PolicyFunctions. Sihui Wang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy

  8. Extending and Applying the GaussianProcess Autoregressive Regression…

    https://www.mlmi.eng.cam.ac.uk/files/mlmi_thesis_justin_bunker.pdf
    18 Nov 2019: Extending and Applying the GaussianProcess Autoregressive Regression. Model. Justin Bunker. Supervisor:Dr. Richard E. Turner. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in

  9. Bayesian Neural Networks for K-Shot Learning

    https://www.mlmi.eng.cam.ac.uk/files/swiatkowski_dissertation.pdf
    30 Oct 2019: prior) 200.6 0.4GMM 1-mean (iso) 175.5 0.2. 1-.

  10. 1 Automatically Grading Learners’ English using a Deep Gaussian ...

    https://www.mlmi.eng.cam.ac.uk/files/sebastian_popescu_8224831_assignsubmission_file_sgp34_sebastiangabrielpopescu.pdf
    30 Oct 2019: 1. Automatically Grading Learners’. English using a Deep Gaussian Process. Sebastian Gabriel Popescu. Department of Engineering. University of Cambridge. A dissertation submitted to the University of Cambridge in partial. fulfilment of the

  11. Bayes By Backprop Neural Networks forDialogue Management Christopher…

    https://www.mlmi.eng.cam.ac.uk/files/tegho_dissertation.pdf
    30 Oct 2019: Bayes By Backprop Neural Networks forDialogue Management. Christopher Tegho. Queens’ College. MPhil Machine Learning,Speech and Language Technology. August 11th, 2017Cambridge University. I, Christopher Tegho of Queens’ college, being a

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