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21 - 29 of 29 search results for Economics test |u:www.mlmi.eng.cam.ac.uk where 4 match all words and 25 match some words.

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  2. Graph Representation Learning for Child Mental Health Prediction

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/graph_representation_learning_for_child_mental_health_prediction.pdf
    25 Nov 2022: 49. 6.1 Comparison of AUROC Model Performance on Test Set for Feature Propa-gation of Missing Data. ... 57. 6.2 Comparison of AUROC Model Performance on Test Set for Feature Propa-gation of Output Label.

  3. Effectiveness of SSL Representations for Source Separation

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/effectiveness_of_ssl_representations_for_source_separation_reduced.pdf
    25 Nov 2022: vi. reached 2.1% and 2.4% on AMI test and development sets. ... 53. 5.4 The comparison of transcription systems with different prior information.’Test set/Development set’ cpWER-us is provided.

  4. Global Inducing Point Posterior Approximations for Federated Bayesian …

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/global_inducing_point_posterior_approximations_for_federated_bnns.pdf
    25 Nov 2022: List of tables. 5.1 Test set log-likelihoods of classification experiments. 395.2 Wall-clock time of classification experiments. ... Note that this term is also used by the deep learningcommunity at large to refer to evaluation of the model at test time.

  5. Controlling Hallucination while Generating Text from Structured Data…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/controlling_hallucination_while_generating_text.pdf
    24 Jan 2022: ing a different purpose:. • WebNLG: generalisation to unseen topics in the test set. •

  6. Understanding and Fixing the Modality Gap in Vision-Language Models

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/understanding_and_fixing_the_modality_gap_in_vision-language_models_reduced.pdf
    25 Nov 2022: We then conduct toy simulations to test ourhypotheses by digging into the properties of the embedding space. ... The goal is to maximise classification performance on a test set by only using theseCK labelled samples.

  7. Beyond independent masking in tabular self-supervision

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/beyond_independent_masking_in_tabular_self-supervision.pdf
    25 Nov 2022: The MNIST dataset (LeCun et al., 2010) contains 60, 000 training and 10, 000 test grayscale28 28 handwritten digits, see Figure 3.4. ... Each image was pre-processed by applying min-maxnormalization. The provided test set of 10, 000 images were used.

  8. Improving Machine Learning Systems by Eliciting and Incorporating…

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/improving_ml_systems_with_additional_human_knowledge_16.11.22.pdf
    25 Nov 2022: authors collect M 51 annotators for each of the CIFAR-10 (Krizhevsky, 2009) test setimages.

  9. Domain Generalisation for Robust Model-Based Offline Reinforcement…

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/domain_generalisation_for_robust_model_based_offline_rl_0.pdf
    1 Dec 2022: distributional shiftis more extreme than those observed at training/test time (Krueger et al., 2020). ... That is, it is assumed that both the training and test data have been drawnfrom the same distribution.

  10. Scalable Bayesian Inference for Probabilistic Spectrotemporal Models…

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/scalable_bayesian_inference_for_probabilistic_spectrotemporal_models.pdf
    5 Dec 2022: Scalable Bayesian Inference forProbabilistic Spectrotemporal Models of. Type Ia Supernovae. Ana Sofía Uzsoy. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Churchill College

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