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18 Nov 2019: The MNIST test set digits have been projected onto the latent space and are displayedwith a different colour per class. ... Fig. 2.8 MNIST test-set digits with pixels randomly dropped and corresponding VAEAC inpaintings.

6 Nov 2019: Fairness in Machine Learning withCausal Reasoning. Philip Ball. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning, Speech and Language. Technology. Sidney

18 Nov 2019: SPNshave achieved competitive results on numerous tasks. A good test for deep architectures is that of image completion, where it is essential todetect deep structure.

18 Nov 2019: Distributed Variational Inferenceand Privacy. Xiping Liu. Supervisor: Dr Richard E. Turner. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine

30 Oct 2019: 53. Chapter 1. Introduction. 1.1 Optimization. Optimization problems are widespread in science, engineering, economics and finance.For example, regional electricity grid system operators (ISOs) optimise the productionof electricity (solar, wind

18 Nov 2019: TCAV• Test preliminary results with more concepts and classes• Show the change in TCAV score for how high/low level concepts throughout layers• Use the deep dream method to visualized learned ... Test the learned representations on downstream tasks.

30 Oct 2019: LA (train)LA (test)LB (train)LB (test). 0.0 0.2 0.4 0.6 0.8 1.0Training samples evaluated 1e8. ... Sigmoid (train)Sigmoid (test)ReLu (train)ReLu (test)Tanh (train)Tanh (test). • Increasing thedepth of theencoder.

30 Oct 2019: Pooled Images. Performance of Active Learners. Test set accuracy with Number of Queries. ... Test Error Results on MNIST for 100 and 1000 labelled training samplesTest error % with number of used labels 100 1000.

14 Nov 2019: Model #Units Test ErrorBBB 400 1.79%Dropout 400 1.72%. Model #Units Test ErrorBBB 800 1.92%Dropout 800 1.45%.

30 Oct 2019: inferes classes for test examples from the new classes.Phase 4. learns weights for the new classes based on the prior and K examples.Phase 3. ... training classes. test classes. lots of subsets. Comparison of models for the prior.

6 Nov 2019: 110. 100. 90. L. MNIST, Nz = 3. LB trainLB testLA trainLA test. ... 110. 100. 90. L. MNIST, Nz = 20. LB trainLB testLA trainLA test.

15 Nov 2019: Ha = 1.2. In the above example, two factors are principally responsible for the largealeatoric entropy: the high the economic status of the population andthe low pupil-teacher ratio.

6 Nov 2019: 36. 4.5 Skeleton Hierarchical Structure. 37. 4.6 (a) is the original test walking sequence. ... amc file. These local representations are used to train and test various modelarchitectures of RGPs.

6 Nov 2019: 444.3 T-SNE visualization of training z. 444.4 T-SNE visualization of test Ho. ... 444.5 T-SNE visualization of test z. 44. List of tables. 4.1 SemEval-2010 Task 8 dataset statistic.

6 Nov 2019: We augment the inference networks (both fully-connected and convolutional networks) invanilla Variational Auto-Encoders (VAE) with HVI and test the model on different datasetsto prove the effectiveness of combining variational ... To test the performance

30 Oct 2019: all-std) and an ‘mc’approximation - with dropouts at test time (ip-mc,all-mc). ... Neural Networks with dropout-approximation at test time were not found to be ro-bust to adversarial images.

30 Oct 2019: At test time a zero-one loss is used, meaning sequences of operations need be entirelycorrect to receive zero loss. ... in [7], without any strong supervision. Despitethe new marginal objective function - and decrease in training loss - it is found that

18 Nov 2019: 39. 6 Average and std. test predictive log-likelihood (LL), test error, and testexpected calibration error (ECE) (with M = 10 bins). ... We test NeuralLinear architectures on Fashion MNIST and SVHN datasets. We averageover 5 different runs.

14 Nov 2019: We learnunsupervised sentence embddings by training a Neural Statisti-cian on 2 million Wikipedia sentences and we test the sentenceembeddings on a sentence similarity task (SentEval), definingsimilarity as the divergence between

30 Oct 2019: However, at test time, all hidden activations are left untouched (e.g.

30 Oct 2019: 3.3.2 Some analysis. Although the programmatically generated sentences are considerably more predictable andless noisy than real life instructions, we believe they provide enough variety to test theperformance of our

30 Oct 2019: The data-driven approach learnshuman behaviour from training data acquired from human test users – another MachineLearning problem in itself.

30 Oct 2019: In the end, we test their performance on MNISTdataset and the Mixflow model not only achieves the best log likelihood but also producereasonable images compared to state-of-art DCGAN generation.

6 Nov 2019: 354.2a Validation accuracy. 354.2b Training loss. 35. 4.3 ROC curve and confusion matrix for the binary classifier usingthe test set.

30 Oct 2019: Improving Sample Efficiency forGradient-based Policy Optimisation;. with an Application to Structured PolicyFunctions. Sihui Wang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy

6 Nov 2019: p(y) = N(y; 0,Kn,n s 2noiseI), (2.13). and the predictive mean and variance at test x are given by. ... and K,n is the matrix formed by evaluating the kernel function at pairs formed bytaking a single test point and a single training point.

18 Nov 2019: Therefore, It is worthwhile to investigate the stopping criteria of the expectedimprovement.After some tests on the decisions made by the expected improvement, it is observed thatit always begin with an

18 Nov 2019: In other words, for a set of test points for which we want predictions, thekernel is used to construct a covariance matrix for the respective function values at thosepoints. ... Also, given that a GP has N observations and we wantto compute the posterior

30 Oct 2019: Besides the SDS management, the tool kit also providesa large set of domains and the possibility to use a simulated user in order to test the model.As part of this

6 Nov 2019: a) Fidelity test for fake phone aa[2] (b) Fidelity test for fake phone aa[3]. ... Table 1: Top1, Top3 and Top5 Classification Accuracy. PhoneGenerated fake data Test set data.

30 Oct 2019: The output ŷ given theinput test data item x̂ is given by:.

30 Oct 2019: into a training, development, and test set, comprising of 350, 50 and 33-page. ... on future test samples. In [8], Cohn et. al propose a statistically optimal.

30 Oct 2019: viii Table of contents. 6.3 Tests of generated samples. 386.3.1 Comparison task. ... 32 Experiments. this case no separate test set was created as the number of training data samples is very limited.

15 Nov 2019: Future Experiments. • Test differentially private PVI on various models• 1-dimensional regression model• Multi-dimensional regression models• Non-linear models, like Bayesian neural networks. •

30 Oct 2019: This allows us to maintain a representative set of points of size m which we useto approximate our test points. ... A string kernel [27][7] can then be used to test similarity with another action by counting the co-occurrence of (possibly skipped) n-grams

30 Oct 2019: These acoustic conditions are normally variousin training and test data, and cause mismatches. ... This SD DNN model can then used to decode any test datasets directly.

30 Oct 2019: test or evaluation dataset. The perplexity is essentially the distance between a predicted. ... higher overall posterior probabilities) on evaluation and test datasets. Perp = 2H (2.3).

30 Oct 2019: 28. 3.3 Test accuracy and model fitting using Dropout Max Entropy acquisitionfunction. ... 29. 3.4 Test accuracy and model fitting using Dropout Bayes Segnet acquisitionfunction.

6 Nov 2019: They train and test theiragent on different maps. It is possible for the agent to learn the game without externalrewards, since the goal of finding the vest can be reformulated as

18 Nov 2019: Ifan event in a test set is unseen in the training set, Equation 2.4 would simply assign a zeroprobability to that event. ... Smoothing methods can mitigate the data sparsity problem. The relative frequenciesobtained from seen events can be subtracted

30 Oct 2019: with 5 examples from each and recommend this a standard test proceedure for. ... We attempt to follow their test procedure so we can benchmark our performance.

30 Oct 2019: Accordingly,the model attains relatively low likelihoods over the test-data (227) 3.1 when compared with theDGP (808). ... data, The DGP saw improvements in both the MS error and the likelihoodswhen modelling the test-data.

18 Nov 2019: Probabilistic Bellman Consistency inReinforcement Learning. Luca Biggio. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Robinson College August 2019. Declaration. I, Luca

30 Oct 2019: 497.2 User information form presented after clicking “Test Yourself”. 507.3 Question response interface used for all questions. ... We conclude this chapter by quantitatively evalu-ating our final model in test-set loss and training time, and

30 Oct 2019:  Training data from Gujarat Indian speakers.  Small amount of crowd-sourced test data (approximately 25 hours).

30 Oct 2019: Test of English as a Foreign Language (TOEFL) or Cambridge English Advanced (CAE) or as a starting point to a career in an English-speaking country by offering niche orientated examinations ... business context  Part 5 : test takers must imagine that

6 Nov 2019: probabilities at test time. Future Experiments. • Impact of different p during training.• Train on noisier and more complicated data.

30 Oct 2019: Auto-Encoding with StochasticExpectation Propagation in Latent. Variable Models. Vera Gangeskar Johne. Department of Engineering. University of Cambridge. This dissertation is submitted for the degree of. Master of Philosophy. Fitzwilliam College

6 Nov 2019: Stochastic gradient MCMC. Figure: Metropolis-Hasting (blue), Barker (orange), and noise adaptive acceptance test (green). ... The noise adaptive acceptance test is a novel approach to reduce the bias of stochastic gradient HMC.

18 Nov 2019: Table 3: Claims : Observations per Label. Amount of claims per label for the training, development (dev), test and reserved set.