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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

24 Jan 2022: 24. 2.3.1 Automatic metrics for measuring faithfulness. 24. 2.3.2 Semantic evaluation with neural models. ... 24. measured through a comparison of n-grams. For instance, BLEU (Bilingual.

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.

15 Nov 2021: 23. 3.4 Experimental Setup. 243.4.1 Model Configuration. 243.4.2 Experiment Size. 24.

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.

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-.

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

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.

9 Dec 2022: 23. 4.3.1 Prompt format. 23. 4.3.2 Building the prompt embedding. 24.

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