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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. 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.

  3. Better Batch Optimizer

    https://www.mlmi.eng.cam.ac.uk/files/poster_a1_portrait.pdf
    18 Nov 2019: log p(y|X) = 12y>Zy1. 2log |K|1. 2log |A|n. 2log 2π. The strategy is to firstly optimize τ2 with Newton op-timization method and compute θ2 by the

  4. Importance Weighted AutoencodersJ. Rampersad, C.Tegho & S.…

    https://www.mlmi.eng.cam.ac.uk/files/d421a_poster_importance_weighted_autoencoders.pdf
    6 Nov 2019: Importance WeightedAuto-Encoder. IWAE uses the same architecture as VAE but op-timises a tighter bound on logp(x) corresponding to.

  5. 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.

  6. Efficiently Approximating Gaussian Process Regression

    https://www.mlmi.eng.cam.ac.uk/files/efficiently_approximating_gaussian_process_regression_david_burt.pdf
    6 Nov 2019: Typi-cally, M N and inference can be performedin O(nm2). All parameters in g and µ can be op-timized variationally (Titsias,2009).

  7. Manifold Hamiltonian Dynamics for Variational Auto-EncodersYuanzhao…

    https://www.mlmi.eng.cam.ac.uk/files/manifold_hamiltonian_dynamics_for_variational_auto-encoders_yichuan_zhang_poster_final.pdf
    6 Nov 2019: parametric form we can then op-timize the lower bound to get a good approximation to the true posterior.(2) Optimizing the lower boundFor most qt and rt, the lower bound

  8. 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). •

  9. Fashion Products Identification UsingBayesian Latent Variable Models…

    https://www.mlmi.eng.cam.ac.uk/files/dissertation_areebsiddique.pdf
    6 Nov 2019: Fashion Products Identification UsingBayesian Latent Variable Models. Areeb Ur Rehman SiddiqueDepartment of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree of Master of Philosophyin Machine Learning, Speech and

  10. 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-.

  11. 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,

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