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Fashion Products Identification UsingBayesian Latent Variable Models…
https://www.mlmi.eng.cam.ac.uk/files/dissertation_areebsiddique.pdf6 Nov 2019: The objective is topredict the value f(x) for a test data point x. ... The predictive distribution gives the dataspace representation for a test latent space point. -
Tradeoffs in Neural Variational Inference
https://www.mlmi.eng.cam.ac.uk/files/cruz_dissertation.pdf30 Oct 2019: 30. 5.3 Pose data: average ELBO over the test set (175,638 samples). ... 5.9 Pose data: average ELBO over the test set (175,638 samples). -
MergedFile
https://www.mlmi.eng.cam.ac.uk/files/de_jong_thesis.pdf6 Nov 2019: The smaller network can then be used for fast approximations to theBayesian teacher network at test time. ... This results in high storage savings, however at test time,the full weight matrix needs to be restored again, resulting in no memory savings. -
Combining Sum Product Networks and Variational Autoencoders
https://www.mlmi.eng.cam.ac.uk/files/thesis_pingliangtan.pdf6 Nov 2019: 437.6 Test gap (train evidence - test evidence) at peak test performance of SP-VAE vs VAE. ... Notice the green curve. 487.10 Test evidence on SVHN against fraction of SPN parameters in sum nodes. -
Bayesian Neural Networks for K-Shot Learning
https://www.mlmi.eng.cam.ac.uk/files/swiatkowski_dissertation.pdf30 Oct 2019: They apply their method to few-shot classi-fication as an example test bed. ... Additionally to test accuracies, we also report the. 3.2 Implementation and experimental setup 27. -
Sequential Neural Models with Stochastic Layers
https://www.mlmi.eng.cam.ac.uk/files/d402k_poster_sequential_neural_models_with_stochastic_layers.pdf6 Nov 2019: Figure 1d shows the average cross entropy for theheld-out test data as a function of the differentdatasets and stochastic variable dimension. -
Poster_FINAL.key
https://www.mlmi.eng.cam.ac.uk/files/defending_a_speech_recogniser_against_adversarial_examples_ainecahill.pdf6 Nov 2019: Success rate of adversarial examples. • Model accuracy vs. % adversarial examples in test set. • -
Generative Adversarial Networks for Speech Recognition Data…
https://www.mlmi.eng.cam.ac.uk/files/tianyu_wu_mphil-thesis.pdf6 Nov 2019: 374.5 Fidelity test for fake feature maps generated by unconditional GANs: ’aa’. ... aa’ ). 354.3 Classification accuracies for CGANs’ samples and TIMIT test set (phone:. -
mphilthesis.def
https://www.mlmi.eng.cam.ac.uk/files/graziani_dissertation.pdf30 Oct 2019: 446.5 Comparison of development and test PPL values of the TF-RNNLM. ... TF-RNNLM-GRU). 486.8 Development and Test PPL for each of the dierent systems. -
Constrained Bayesian Optimization for Automatic Chemical Design
https://www.mlmi.eng.cam.ac.uk/files/griffiths_dissertation.pdf30 Oct 2019: The standard error is given for 5 separatetrain/test set splits of 90/10. ... TheResults of 5 Separate Train/Test Set Splits of 90/10 are Provided. -
Waveform Level Synthesis
https://www.mlmi.eng.cam.ac.uk/files/dou_thesis.pdf30 Oct 2019: 123.2 test NLL in bits for 2-tier and 3-tier models. 20. ... Table 3.2 test NLL in bits for 2-tier and 3-tier models. -
Designing Neural Network Hardware Accelerators Using Deep Gaussian…
https://www.mlmi.eng.cam.ac.uk/files/havasi_dissertation.pdf30 Oct 2019: 424.2 Training and test log-likelihoods during training. 434.3 Runtime of the training process for the two implementations. ... The test log-likelihood of the GPmodel was 1.200.06 as opposed to 0.610.04 of DGPs and 0.480.05 of JointDGPs at 300 training -
Neural Network Compression
https://www.mlmi.eng.cam.ac.uk/files/okz21_thesisfinal.pdf6 Nov 2019: Tests show that throughthis technique, the drop in accuracy for MobileNet network for ImageNet challenge reducesby a mere 1-2% [22]. ... The dataset consists of the 60,000 training and 10,000 test 28 28 pixel examples of the 10 digits. -
Augmenting Natural Language Generation with external memory modules…
https://www.mlmi.eng.cam.ac.uk/files/minglong_sun_mphil_thesis.pdf6 Nov 2019: 385.2.2 Main results. 385.2.3 Models’ performances on unseen test data. 405.2.4 Discussions. ... jointly on SF Multi-Domain. 405.8 Numbers of DA in unique and non-overlap test sets across the domains in. -
Interpretable Machine Learning Tyler Martin Supervisor: Dr. Adrian…
https://www.mlmi.eng.cam.ac.uk/files/tam_final_reduced.pdf18 Nov 2019: This project explores many. aspects of the algorithm, tests its limits, and proposes best practices for practitioners. -
Variational Inference in Deep Directed Latent Variable ModelsRiashat…
https://www.mlmi.eng.cam.ac.uk/files/variational_inference_in_deep_directed_latent_variable_models.pdf30 Oct 2019: Experimental Results. Figure below shows test set performance dor different dimensionality of latent space. -
Bayesian Deep Generative Models for Semi-Supervised and Active…
https://www.mlmi.eng.cam.ac.uk/files/gordon_dissertation.pdf30 Oct 2019: A similar pipeline is followed for new test data. The second (M2) approach proposed extending the VAE model to include labels, asdepicted in Figure 3.1.
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