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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.
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