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51 - 60 of 62 search results for katalk:za33 24 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 62 match some words.

  1. Results that match 1 of 2 words

  2. Understanding Uncertainty in Bayesian Neural Networks

    https://www.mlmi.eng.cam.ac.uk/files/mphil_thesis_javier_antoran.pdf
    18 Nov 2019: qφ (z) =. qφ (z|x)p(x)dx (2.24). does not match the prior p(z).

  3. Interpretable Machine Learning Tyler Martin Supervisor: Dr. Adrian…

    https://www.mlmi.eng.cam.ac.uk/files/tam_final_reduced.pdf
    18 Nov 2019: 3.2 DeepDream. 21. 3.3 Activation Perturbation. 24. 3.4 CAV Accuracy. 25.

  4. Gong_dissertation

    https://www.mlmi.eng.cam.ac.uk/files/gong_dissertation_reduced.pdf
    30 Oct 2019: The objective function is. infQ(zzz|xxx). Exxxpd EzzzQ(zzz|xxx)[c(xxx,G(zzz))] λ D(Q(zzz)||ZZZ)) (2.24). where Q(zzz|xxx) is the encoder network, c(,)

  5. Model Uncertainty for Adversarial Examples using Dropouts

    https://www.mlmi.eng.cam.ac.uk/files/ambrish_rawat_8224901_assignsubmission_file_rawat_ambrish_thesis1.pdf
    30 Oct 2019: In 2015 IEEE Conference. 24 References. on Computer Vision and Pattern Recognition (CVPR), pages 427–436.

  6. Optimising spoken dialogue systems using Gaussianprocess…

    https://www.mlmi.eng.cam.ac.uk/files/thomas_nicholson_8224691_assignsubmission_file_done.pdf
    30 Oct 2019: 24. Reducing action selection complexity. 25Clustering of actions. 26. Cold Start. ... The authors use Rollout Classification Policy Itera-tion[24] (RCPI), policy iteration approach that generate training examples by using Monte-Carlo(MC).

  7. Probabilistic Bellman Consistency in Reinforcement Learning

    https://www.mlmi.eng.cam.ac.uk/files/biggio_dissertation.pdf
    18 Nov 2019: Probabilistic Bellman Consistency inReinforcement Learning. Luca Biggio. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Robinson College August 2019. Declaration. I, Luca

  8. Bachbot Marcin Tomczak Department of EngineeringUniversity of…

    https://www.mlmi.eng.cam.ac.uk/files/marcin_tomczak_8224841_assignsubmission_file_tomczak_dissertation.pdf
    30 Oct 2019: A similar approach is based on grammars. This notion has been introduced in [24].

  9. 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: Specifically in. 24. our example, the label of data points xi D R2 follows a Bernoullidistribution. ... MCMC (2013); and in 2014, Anglican, from Wood’sgroup, consists various inference algorithms [16, 23, 24, 25].

  10. Disentangling Sources of Uncertainty for Active Exploration

    https://www.mlmi.eng.cam.ac.uk/files/disentangling_sources_of_uncertainty_for_active_exploration_reduced.pdf
    18 Nov 2019: 12. 3.1 Cart-pole PILCO environment. 24. 3.2 Pendubot PILCO environment. 24. ... p(|,X̃,x̃,σ 20 ,σ 2n ) = N(|µ,σ. 2). (2.22). whereµ =. 1σ 2n. ϕ(x̃)A1Φ (2.23). σ 2 = σ2n ϕ(x̃). A1ϕ(x̃). (2.24).

  11. Compression without Quantization Gergely Flamich Department of…

    https://www.mlmi.eng.cam.ac.uk/files/compression_without_quantization_flamich_reduced.pdf
    18 Nov 2019: Compression without Quantization. Gergely Flamich. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. St John’s College August 2019.

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