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21 - 30 of 141 search results for KaKaoTalk:PC53 24 / |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 141 match some words.

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  2. Breaking the Limits of Diffusion Models via Continuous Dynamical…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/breaking_the_limits_of_diffusion_models.pdf
    14 Nov 2023: 24. 3.3 Illustration of our custom ODE block capturing continuous-time dynamics.The block processes outputs from the derivative function approximator anduses them to delineate the data’s evolutionary trajectory.

  3. 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).

  4. Efficiently-Parametrised Approximate Posteriors in Pseudo-Point…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/efficiently_parametrized_approximate_posteriors.pdf
    15 Nov 2021: exp(L1)p(u)q(u)q(u). du. (L1 log p(u)log q(u))q(u)du. = Eq(u) (L1 log p(u)log q(u)) L2 (2.24). ... L2 in Hensman et al. (2013) as follows (see equation 2.24 and 2.20 for the definition of L2and L1 respectively):.

  5. Graph Neural Stochastic Differential Equations

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/graph_neural_stochastic_differential_equations.pdf
    17 Nov 2023: 2.3.6 Comparison: Oversmoothing in GN-ODE vs. Standard GNN. 24. 3 Graph Neural Stochastic Differential Equations 26. ... 24. 3.1 The left image illustrates the political compass of voters while the rightimage presents their social circles, with colors

  6. Well-Calibrated Bayesian NeuralNetworks On the empirical assessment…

    https://www.mlmi.eng.cam.ac.uk/files/jheek_thesis.pdf
    6 Nov 2019: 𝜃)𝑞𝜙(𝜃) ]. (2.24). 5More generally, the argument that follows holds for any family of distributions 𝑞𝜙(𝜃) where the entropy𝔼[ log 𝑞𝜙(𝜃)] is invariant w.r.t. ... the global reparameterisation trick (2.23).Alternatively,

  7. Contrastive Self-Supervised Learning for Tabular Data

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/contrastive_self-supervised_learning.pdf
    9 Dec 2021: Contrastive Self-Supervised Learningfor Tabular Data. Hugh Bishop. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Wolfson College

  8. Extending Deep GPs: Novel Variational Inference Schemes and a GPU…

    https://www.mlmi.eng.cam.ac.uk/files/maximilian_chamberlin_8224701_assignsubmission_file_mc.pdf
    30 Oct 2019: 24. Chapter 1. Introduction: The Deep GaussianProcess Model. 1.1 What are Deep GPs?

  9. Knowledge Distillation for End-to-End Automatic Speech Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/knowledge_distillation_for_end-to-end_asr.pdf
    9 Dec 2021: 24. xii Table of contents. 3.2.1 Frame-level KD in CTC. 243.2.2 Sequence-level KD in CTC. ... However, the definition of. 24 Knowledge Distillation in ASR. "knowledge" varies from area to area.

  10. Adapting Pretrained Vision-Language Models in Medical Domains

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/adapting_pretrained_vision-language_models.pdf
    28 Nov 2023: Adapting Pretrained Vision-LanguageModels in Medical Domains. Liangchen Li. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Clare

  11. 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: the best MCR in Cambridge. I’ve absolutely loved 24 Parkside, everyone here had made. ... h, ,i (2.24). Where is a non-terminal symbol, while , 2 (X [ V) are a string of terminalsand non-terminals in the source and target languages respectively, where

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