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NPL – Learning through Weak SupervisionJ. Rampersad, N. Kushman ...
https://www.mlmi.eng.cam.ac.uk/files/jamesrampersad_jr704poster.pdf30 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. -
Better Batch Optimizer
https://www.mlmi.eng.cam.ac.uk/files/poster_a1_portrait.pdf18 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 -
Importance Weighted AutoencodersJ. Rampersad, C.Tegho & S.…
https://www.mlmi.eng.cam.ac.uk/files/d421a_poster_importance_weighted_autoencoders.pdf6 Nov 2019: Importance WeightedAuto-Encoder. IWAE uses the same architecture as VAE but op-timises a tighter bound on logp(x) corresponding to. -
Neural Program Lattices
https://www.mlmi.eng.cam.ac.uk/files/rampersad_dissertation.pdf30 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. -
Efficiently Approximating Gaussian Process Regression
https://www.mlmi.eng.cam.ac.uk/files/efficiently_approximating_gaussian_process_regression_david_burt.pdf6 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). -
importance-weighted-autoencoders-poster (1)
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_advanced_machine_learning_posters/importance_weighted_autoencoders_poster_1_2022.pdf17 May 2022: Importance Weighted AutoencodersFederico Barbero, Kaiqu Liang, Haoran Peng. 📖 Generative model capable of learning latent representations from data z x. Architecture. Density Estimation. 1.Kingma, D. P., and Welling M., ”Auto-encoding -
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.pdf6 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 -
Natural Language to Neural Programs
https://www.mlmi.eng.cam.ac.uk/files/simig_dissertation.pdf30 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). • -
Fashion Products Identification UsingBayesian Latent Variable Models…
https://www.mlmi.eng.cam.ac.uk/files/dissertation_areebsiddique.pdf6 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 -
Pathologies of Deep Sparse Gaussian Process Regression
https://www.mlmi.eng.cam.ac.uk/files/diaz_thesis.pdf30 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-.
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