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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.