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41 - 50 of 141 search results for katalk:za33 24 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 141 match some words.

  1. Results that match 1 of 2 words

  2. Training Restricted BoltzmannMachines Using High-Temperature…

    https://www.mlmi.eng.cam.ac.uk/files/pawel_budzianowski_8224891_assignsubmission_file_budzianowski_dissertation.pdf
    30 Oct 2019: 24. 3.4 Contrastive divergence. 253.4.1 Persistent contrastive divergence. 25. 3.5 Learning using extended mean field approximation. ... Denote by:. θ = Ep(f(X)) =f(x)p(x)dx. 24 Learning of Boltzmann machine.

  3. Multilingual Models in Neural Machine Translation

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/multilingual_models_in_neural_machine_translation.pdf
    24 Nov 2023: 23. 3.4 Summary. 24. 4 Results and Discussions 254.1 In-context Learning for NMT. ... The loss function can be any string-to-string distance function, or the negative of string-to-string alignment function such as BLEU [24] and BLEURT [34] (see Section

  4. GPT-3 for Few-Shot Dialogue State Tracking

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/gpt_3_for_few_shot_dialogue_state_tracking.pdf
    15 Nov 2021: 23. 3.4 Experimental Setup. 243.4.1 Model Configuration. 243.4.2 Experiment Size. 24.

  5. Distilling and Forgetting in Large Pre-Trained Models

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/distilling_and_forgetting_in_pre-trained_models_public.pdf
    17 Nov 2023: Extensively usedPython libraries include:. • Standard packages: numpy 1.24.3, typer 0.9.0, jiwer 3.0.2, pandas 2.0.2. • ... This tendency is motivated by the scaling lawsintroduced by OpenAI in [35] and can be witnessed for speech as well (see Figure

  6. Autoregressive Conditional Neural Processes

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/autoregressive_conditional_neural_processes_reduced.pdf
    25 Nov 2022: 56. 4.24 In the bottom plot, we see the number of executions (forward passes) ofthe model Qq for each trajectory length.

  7. Spatio-Temporal Variational Autoencoders

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/spatio-temporal_variational_autoencoders.pdf
    19 Feb 2021: 24. 3.1.1 The Probabilistic Model. 243.1.2 The Structured Approximate Posterior. 243.1.3 The Posterior Predictive Distribution. ... The quantityz log. pθ(y, z)qϕ(z). ϕg(ϵ,ϕ) (2.24). is known as the path derivative - it accounts for the dependence on

  8. Tudor Paraschivescu Multi-output Gaussian Process Regression at Scale …

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/multi-output_gaussian_process_regression_at_scale.pdf
    11 Feb 2021: the Nystrom approximation to Gaussian Processes [Williams and Seeger, 2001]. 24 CHAPTER 2. ... Equation 2.24. k = yk Hmk|k1 (2.27). We call this method ‘decorrelate’ since multiplying a sample from a multivariate Gaus-.

  9. Hierarchical Dialogue Management

    https://www.mlmi.eng.cam.ac.uk/files/gordaniello_dissertation.pdf
    30 Oct 2019: k((bt,at),(bt,at))aTt k̃t1(bt,at) > ν (2.24). where. k̃t1(bt,at) = [k((bt,at),(b̃0,ã0)),.,k((bt,at),(b̃m,ãm))]T. at = K̃1t1k̃t1(bt,at). ... 24 Methods. Figure 3.7. Architecture of the BCM for a set of six domains.

  10. Vision Encoders in Visual Question Answering

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/vision_encoders_in_visual_question_answering.pdf
    9 Dec 2022: 23. 4.3.1 Prompt format. 23. 4.3.2 Building the prompt embedding. 24.

  11. Improving Uncertainty Quantification in Regression Problems through…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/improving_uncertainty_quantification_in_regression_problems.pdf
    23 Nov 2023: Improving UncertaintyQuantification in RegressionProblems through Conformal. Training. Johannes Vallikivi. MPhil in Machine Learning and Machine IntelligenceDepartment of Engineering. University of Cambridge. This dissertation is submitted for the

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