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Better Batch Optimizer
https://www.mlmi.eng.cam.ac.uk/files/poster_a1_portrait.pdf18 Nov 2019: Therefore, we obtain another GP,. g(x) GP(h(x)T b,k(x, x′) h(x)T Bh(x′)). ... Preliminary Result. (a). (b)Figure: Predictions made after 1 (a) and 10 (b) iterations. -
Hierarchical Dialogue Management
https://www.mlmi.eng.cam.ac.uk/files/gordaniello_dissertation.pdf30 Oct 2019: Vπ(b) = Eπ{Rt | bt = b}=. b′p(b′ | b,π(b))[r(b,a)Vπ(b′)]db′ (2.12). ... Qπ(b,a) = Eπ{Rt | bt = b,at = a}=. b′p(b′ | b,a)[r(b,a) Qπ(b′,π(b′))]db′ (2.13). -
Sample efficient deep reinforcement learning for dialogue systems…
https://www.mlmi.eng.cam.ac.uk/files/weisz_dissertation.pdf30 Oct 2019: Algorithm 3 Off-policy Monte Carlo control1: Initialise Q arbitrarily, N(b,a) = D(b,a) = 0 b B,a A π. ... 7: Choose a′ ε -greedily8: Q(b,a) Q(b,a) α(r γ Q(b′,a′)Q(b,a))9: b b′,a a′. -
Extending and Applying the GaussianProcess Autoregressive Regression…
https://www.mlmi.eng.cam.ac.uk/files/mlmi_thesis_justin_bunker.pdf18 Nov 2019: ba. ba. k′(x,x′)dxdx′ =. { b. b. k′. } 2. { a. ... b. k′. }. { a. a. k′. }= k(b,b) 2k(a,b) k(a,a). -
Better Batch Optimizer
https://www.mlmi.eng.cam.ac.uk/files/dissertation.pdf18 Nov 2019: 62A.2 Line Search. 63A.3 zoom. 63. Appendix B Smoothing Spline 65. ... andβ N (b,B) are additional parameters (as mentioned in Section 2.5.3). -
Bayes By Backprop Neural Networks forDialogue Management Christopher…
https://www.mlmi.eng.cam.ac.uk/files/tegho_dissertation.pdf30 Oct 2019: kB(b,b′)kA(a,a. ′) (2.17). The prior for the residual follows Q(b,a) N(0,σ2). ... k((b,a), (b′,a′)) = 〈b,b′〉δa(a′) (2.19). The linear kernel assumes the elements of the space are features. -
Curiosity-Driven Reinforcement Learning for Dialogue Management
https://www.mlmi.eng.cam.ac.uk/files/paulawesselmann_mlsalt.pdf6 Nov 2019: Vπ(b) = a. π(a,b)b′. r. p(b′,r|b,a)(r γVπ(b′)) (2.8). relating the value function of state b to the value function of its ... 7: Q(b,a) Q(b,a) α[r γ max′a Q(b′,a′)Q(b,a)]8: b b′. 9: until beliefstate b is terminal10: until convergence -
thesis
https://www.mlmi.eng.cam.ac.uk/files/burt_thesis.pdf6 Nov 2019: Define a new kernel on the interval [a,b] by,. k̃(M)(x,x0) = (b a)M. ... or sin(wmx)) with wm harmonic on the interval [a,b], and suggested Fourier analaysis as amotivation for these features. -
Designing Neural Network Hardware Accelerators Using Deep Gaussian…
https://www.mlmi.eng.cam.ac.uk/files/havasi_dissertation.pdf30 Oct 2019: 1. KM. at(. 0, x). Matérn kernel with = 5/2. (b) Matérn covariance function. ... a) Full GP model. (b) Sparse GP model. Fig. 2.3 Comparison of full GP and sparse GP models. -
Optimising spoken dialogue systems using Gaussianprocess…
https://www.mlmi.eng.cam.ac.uk/files/thomas_nicholson_8224691_assignsubmission_file_done.pdf30 Oct 2019: k(b, b) = σ2(b b)2. which has been shown [29] to be able to approximate any arbitrary continuous function2. ... Xu(b) = maxau. R(b, a) γP(b|b, a) maxu. Xu(b) (3). where u u1,. -
Stochastic Memory for Sequence Models Making Good Compressors Use ...
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/stochastic_memory_for_sequence_models.pdf2 Mar 2023: E. [b. B. ]=. 1. 8xAs. NumOfTargetBits (x) freq (x). =1. ... 8. CHAPTER 1. INTRODUCTION. an expected bB. of. E. [b. B. ]=. -
Sim2Real With Neural Processes Jonas Scholz Department of Engineering …
https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/sim2real_with_neural_processes.pdf24 Nov 2023: Note that h(i) is a (badx)-dimensionalobject when ρθ is a Convolutional Neural Network (CNN), where a,b N are specifiedby the model architecture. ... 1. 2. y. ContextTarget. Pred Pred. 95%. True True 95%. (b) = 0.1. -
Automating Counterspeech in Dialogue Systems
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/automatingcounterspeechindialoguesystems.pdf25 Nov 2022: B.1 The ratings guide used for the human counterspeech evaluation study, aspresented to the human evaluators. ... 47. B.2 A screenshot taken from the survey presented to human evaluators for thehuman counterspeech evaluation study. -
Optimal PAC-Bayes Bounds and their Variational Approximations
https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/optimal_pac-bayes_bounds.pdf24 Nov 2023: To invert the Bernoulli KL, we define. kl1(x,b) := sup{y [x,1] : kl(x||y) b}. ... The PAC-Bayes λ bound is a special case with a = 1/(1 λ2 ),b = 1/(nλ (1 λ2 )) and c = log(2. -
Incorporating Vision Encoders into Retrieval Augmented Visual…
https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/visual_question_answering_nikolic.pdf24 Nov 2023: 1.1 The example of (a) VQA and (b) KB-VQA image-question pair. ... Answer: 1839. b) KB-VQA example. Question: What kind of bird is pictured here? -
Training Restricted BoltzmannMachines Using High-Temperature…
https://www.mlmi.eng.cam.ac.uk/files/pawel_budzianowski_8224891_assignsubmission_file_budzianowski_dissertation.pdf30 Oct 2019: mh[t 1] = sigm[b Wmv[t]. (mh[t] 12. )T W 2. (mv[t] (mv[t])2. )],. ... This might be summarized as follows:. mh[t 1] = sigm[b Wmv[t]. -
Evaluating the Capabilities of Large Language Models for Spatial and…
https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/evaluating_the_capabilities_of_large_language_models.pdf17 Nov 2023: 7. 4.1 A quantitative evaluation of GPT-4’s understanding of country-level humanpopulations and their impact on the environment, including (a) countrypopulations, (b) life expectancies, and (c) CO2 emissions ... The redcircles denote outliers. 20. 4.2 -
Improving Uncertainty Quantification in Regression Problems through…
https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/improving_uncertainty_quantification_in_regression_problems.pdf23 Nov 2023: 41. B.1 Various metrics measuring conditional coverage and predictive intervalwidth for concrete, community, and star over 30 seeded runs withmeans and half standard deviations reported. ... More specifically, for a given ordered grid B (bi)i{0,.,N } YN 1 -
Spatio-Temporal Variational Autoencoders
https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/spatio-temporal_variational_autoencoders.pdf19 Feb 2021: 8Specifically, lima,b x b. af (x, z)dz = lima,b. ba. x. -
Autoregressive Conditional Neural Processes
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/autoregressive_conditional_neural_processes_reduced.pdf25 Nov 2022: Autoregressive. Conditional Neural Processes. Anthony Buonomo. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Queens’ College August 16, 2022. This work is dedicated to my
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