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4F13 Machine Learning: Coursework #1: Gaussian Processes Zoubin…
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/cw/coursework1.pdf19 Nov 2023: Show and comment on the fit and the hypers, and the predictions forthe test data. -
Unifying Orthogonal Monte Carlo Methods
https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf19 Jun 2024: 3, 4. Approximating kernel matrices: We test the relative er-ror of kernel matrix estimation for the above estimators forthe Gaussian kernel (following the setting of Choromanski& Sindhwani, 2016). ... A kernel two-sample test. J. Mach. Learn.Res., -
Leader Stochastic Gradient Descent (LSGD) for Distributed Training of …
https://mlg.eng.cam.ac.uk/adrian/LSGD_Poster_NeurIPS2019.pdf19 Jun 2024: Test error for the center variable versus wall-clock time. Figure: ResNet20 on CIFAR-10 with 4 workers (on the left) and 16 workers (on the right). ... Test error for the center variable versus wall-clock time. Figure: ResNet20 on CIFAR-10. -
Structured Prediction Models for Chord Transcription of Music Audio…
https://mlg.eng.cam.ac.uk/adrian/icmla09adrian.pdf19 Jun 2024: Table 2 shows p-values for paired t-tests examining outperformance of each model compared to the baseline HMMv approach. ... Figure 2 shows the TOM test accuracies for all the models trained using Hamming distance as before. -
4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/cw/coursework3.pdf19 Nov 2023: c) For the Bayesian model, what is the log probability for the test document with ID 2001? ... Explainwhether, when computing the log probability of a test document, you would use the multinomial withor without the “combinatorial factor”. -
Evaluating and Aggregating Feature-based Model Explanations
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf19 Jun 2024: For Iris [Dua and Graff, 2017], we train our modelto 96% test accuracy. ... 45). Table 2: Faithfulness µF averaged over a test set: (Zero Baseline,Training Average Baseline). -
ML-IRL: Machine Learning in Real Life Workshop at ICLR ...
https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf19 Jun 2024: We evaluate the test set of fours, sevens, and nines with our BNN. ... This group was able to reach an accuracyof 88% on unseen test points. -
Modelling data
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/modelling%20data.pdf19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. • -
Exploring Properties of the Deep Image Prior Andreas…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS_2019_DIP7.pdf19 Jun 2024: To test the nature of these outputs we introduce a novel saliencymap approach, termed MIG-SG. ... To test this, we evaluated the sensitivity of DIP to changes innetwork architecture. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect06.pdf19 Nov 2023: Constraint-Based Learning: Use statistical tests of marginal and conditionalindependence. Find the set of DAGs whose d-separation relations match theresults of conditional independence tests.
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