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11 - 20 of 77 search results for Economics test |u:www.mlmi.eng.cam.ac.uk where 5 match all words and 72 match some words.

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  2. Auto-Encoding Variational Bayes

    https://www.mlmi.eng.cam.ac.uk/files/auto_encoding_var_bayes_d423c.pdf
    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.

  3. Interpreting Uncertainty in Bayesian Neural Networks

    https://www.mlmi.eng.cam.ac.uk/files/javier_poster.pdf
    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.

  4. 3D Human Motion Synthesis with Recurrent Gaussian Processes

    https://www.mlmi.eng.cam.ac.uk/files/mphil_thesis_yeziwei_wang.pdf
    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.

  5. thesis_1

    https://www.mlmi.eng.cam.ac.uk/files/mlsalt_thesis_yixuan_su.pdf
    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. Manifold Hamiltonian Dynamics for Variational Auto-Encoders

    https://www.mlmi.eng.cam.ac.uk/files/thesis_yuanzhao_zhang.pdf
    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

  7. Model Uncertainty for Adversarial Examples using Dropouts

    https://www.mlmi.eng.cam.ac.uk/files/ambrish_rawat_8224901_assignsubmission_file_rawat_ambrish_thesis1.pdf
    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.

  8. Neural Program Lattices

    https://www.mlmi.eng.cam.ac.uk/files/rampersad_dissertation.pdf
    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

  9. Investigating Inference in BayesianNeural Networks via Active…

    https://www.mlmi.eng.cam.ac.uk/files/riccardo_barbano_dissertation_mlmi.pdf
    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.

  10. Towards a Neural Statistician

    https://www.mlmi.eng.cam.ac.uk/files/poster_final.pdf
    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

  11. Memory Networks for Language Modelling

    https://www.mlmi.eng.cam.ac.uk/files/chen_dissertation.pdf
    30 Oct 2019: However, at test time, all hidden activations are left untouched (e.g.

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