Search

Search Funnelback University

Search powered by Funnelback
10 results 20 results 50 results
Relevance Date (Newest first) Date (Oldest first) Title (A-Z) Title (Z-A) Distance URL (A-Z) URL (Z-A) Shuffle
11 - 18 of 18 search results for KaKaoTalk:po03 op |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 18 match some words.

  1. Results that match 1 of 2 words

  2. thesis

    https://www.mlmi.eng.cam.ac.uk/files/james_requeima_8224681_assignsubmission_file_requeimajamesthesis.pdf
    30 Oct 2019: Integrated Predictive EntropySearch for Bayesian Optimization. James Ryan Requeima. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree of. Master of Philosophy. Darwin College August 2016. Declaration. I,

  3. Designing Neural Network Hardware Accelerators Using Deep Gaussian…

    https://www.mlmi.eng.cam.ac.uk/files/havasi_dissertation.pdf
    30 Oct 2019: Designing Neural Network HardwareAccelerators Using Deep Gaussian. Processes. Márton Havasi. Supervisor: Dr. J. M. Hernández-Lobato. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of

  4. Variable length word encodings forneural translation models Jiameng…

    https://www.mlmi.eng.cam.ac.uk/files/jiameng_gao_8224881_assignsubmission_file_j_gao_mphil_dissertation.pdf
    30 Oct 2019: Variable length word encodings forneural translation models. Jiameng Gao. Department of Engineering. University of Cambridge. This dissertation is submitted for the degree of. Master of Philosophy. Peterhouse August 11, 2016. Acknowledgements. Here

  5. Extending Deep GPs: Novel Variational Inference Schemes and a GPU…

    https://www.mlmi.eng.cam.ac.uk/files/maximilian_chamberlin_8224701_assignsubmission_file_mc.pdf
    30 Oct 2019: However, such an approach is not without its diculties.The extra variational parameters “ that emerge from the variational framework need to be op-timised in addition to the model parameters , which

  6. Probabilistic Programming in JuliaNew Inference Algorithms Kai Xu…

    https://www.mlmi.eng.cam.ac.uk/files/kai_xu_8224821_assignsubmission_file_xu_kai_dissertation.pdf
    30 Oct 2019: InTuring, a probabilistic program can be defined using some probabilistic op-erations in a normal Julia program and this program can be executed bysome general inference engines to learn the model

  7. One-shot Learning in DiscriminativeNeural Networks Jordan Burgess…

    https://www.mlmi.eng.cam.ac.uk/files/jordan_burgess_8224871_assignsubmission_file_burgess_jordan_thesis1.pdf
    30 Oct 2019: Silver et al., 2016] have demonstrated the capabilities of high-capacity models op-.

  8. Optimising spoken dialogue systems using Gaussianprocess…

    https://www.mlmi.eng.cam.ac.uk/files/thomas_nicholson_8224691_assignsubmission_file_done.pdf
    30 Oct 2019: Policy optimisation in POMDPs is intractable [22], and while there exist approximate policy op-timisation methods making assumptions specific to the SDS problem (see [40], [54]) they requirethe hand-factorisation of ... Recent work in [48] examined how

  9. Investigating Inference in BayesianNeural Networks via Active…

    https://www.mlmi.eng.cam.ac.uk/files/riccardo_barbano_dissertation_mlmi.pdf
    18 Nov 2019: θ is unfeasible. In a not-ideal circumstance, op-timisation methods have to deal with stochastic gradient estimates. ... That implies how toadapt VI to stochastic minibatch-based backpropagation. In minibatch stochastic op-timisation, for each epoch the

Refine your results

clear all

Date

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.