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  2. Sim2Real With Neural Processes Jonas Scholz Department of Engineering …

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/sim2real_with_neural_processes.pdf
    24 Nov 2023: ŷj = y(C)(tj) =. NCi=1. ϕ (yCi)ψE (tj xCi) (2.7). Here ψE is the encoder basis function, which is chosen to be a squared-exponentialkernel of lengthscale E:.

  3. Establishing a Unified Framework for Iterative Machine Teaching

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/framework_for_iterative_machine_teaching.pdf
    17 Nov 2023: wt = wt1 ηt 1m. m. j=1. l(xtj,y. tj|wt1. ) wt1.

  4. Evaluating Benefits of Heterogeneity in Constrained Multi-Agent…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/evaluating_benefits_of_heterogeneity.pdf
    14 Dec 2023: dTWass-RD(i, j) = W2(rTi ,r. Tj ) (3.10). This modified measure is useful to understand the action distance between agents, but it isstill limited by the fact that it

  5. CONDITIONAL AND LATENT NEURAL PROCESSES Model Architecture William…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_advanced_machine_learning_posters/conditional_and_latent_neural_processes.pdf
    14 Dec 2023: CONDITIONAL AND LATENT NEURAL PROCESSES. Model Architecture. William BakerAlexandra ShawTony Wu. Motivation 1D Regression. Image Completion on MNIST. The University of Cambridge, Advanced Machine Learning. Image Completion on CelebA. Number of

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