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1 - 10 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. 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

  3. thesis

    https://www.mlmi.eng.cam.ac.uk/files/burt_thesis.pdf
    6 Nov 2019: Spectral Methods in Gaussian ProcessApproximations. David R. Burt. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Emmanuel College August 2018. Declaration. I, David R. Burt,

  4. 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,

  5. 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

  6. Pathologies of Deep Sparse Gaussian Process Regression

    https://www.mlmi.eng.cam.ac.uk/files/diaz_thesis.pdf
    30 Oct 2019: The training procedure is then divided into two subsequent rounds:. – First round: Bottom layer GP-mappings, f (1)1 (x), f(1)2 (x) are initialised with the op-.

  7. 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

  8. 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-.

  9. NPL – Learning through Weak SupervisionJ. Rampersad, N. Kushman ...

    https://www.mlmi.eng.cam.ac.uk/files/jamesrampersad_jr704poster.pdf
    30 Oct 2019: reaching the terminal node. y. t1,li. = p(OP)t,la,i1p. t,l. o,i1(i. o. ... defined as the probability of calling the correct elementary op-. eration, conditional on having called preceding elementary operations.

  10. Neural Program Lattices

    https://www.mlmi.eng.cam.ac.uk/files/rampersad_dissertation.pdf
    30 Oct 2019: NTM Neural Turing Machine. OP Perform an elementary operation that effects the world state. ... p(πt) = [[πta = OP]]pta(OP)p. to(π. to)[[π. ta = PUSH]]p. ta(PUSH)p.

  11. Natural Language to Neural Programs

    https://www.mlmi.eng.cam.ac.uk/files/simig_dissertation.pdf
    30 Oct 2019: On a call to an elementary program (OP), the stack of LSTM-s remains unchanged. ... pta = Wahtout determines the action to be taken (PUSH, POP, or OP). •

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