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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.

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

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 Nov 2019: Importance WeightedAuto-Encoder. IWAE uses the same architecture as VAE but op-timises a tighter bound on logp(x) corresponding to.

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).

17 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

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

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). •

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

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-.

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,

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,

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

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

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

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

30 Oct 2019: Silver et al., 2016] have demonstrated the capabilities of high-capacity models op-.

5 Dec 2022: Intrinsic Residual. AsV<latexit

16 Jul 2024: Diffusion Models for Peptide Binding. John Boom. Supervisor: Prof. Pietro Liò. Dr. Pietro Sormanni. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and

25 Jan 2022: Neural ProcessesGinte Petrulionyte Yuriko Kobe Jack Davis. Neural networks (NNs) are effective function approximators, but do not captureuncertainty over their predictions and cannot easily be updated after training. Gaussian Processes (GPs) are