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1 - 20 of 164 search results for Economics test |u:www.mlmi.eng.cam.ac.uk where 13 match all words and 151 match some words.
  1. Fully-matching results

  2. Understanding Uncertainty in Bayesian Neural Networks

    https://www.mlmi.eng.cam.ac.uk/files/mphil_thesis_javier_antoran.pdf
    18 Nov 2019: The MNIST test set digits have been projected onto the latent space and are displayedwith a different colour per class. ... Fig. 2.8 MNIST test-set digits with pixels randomly dropped and corresponding VAEAC inpaintings.
  3. Fairness in Machine Learning withCausal Reasoning Philip Ball…

    https://www.mlmi.eng.cam.ac.uk/files/ball_thesis.pdf
    6 Nov 2019: Fairness in Machine Learning withCausal Reasoning. Philip Ball. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning, Speech and Language. Technology. Sidney
  4. Sum-Product Copulas

    https://www.mlmi.eng.cam.ac.uk/files/ramonacomanescu-thesis.pdf
    18 Nov 2019: SPNshave achieved competitive results on numerous tasks. A good test for deep architectures is that of image completion, where it is essential todetect deep structure.
  5. Multilingual Models in Neural Machine Translation

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/multilingual_models_in_neural_machine_translation.pdf
    24 Nov 2023: processing. 29. 4.5 Examples of translation hypotheses from the test set of WMT’21 Chinese-English. ... In this project, we test the impact of using 2 to 32 demonstrationexamples.
  6. Distributed Variational Inferenceand Privacy

    https://www.mlmi.eng.cam.ac.uk/files/dissertation_-_xiping_liu.pdf
    18 Nov 2019: Distributed Variational Inferenceand Privacy. Xiping Liu. Supervisor: Dr Richard E. Turner. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine
  7. A Policy Agnostic Framework for Post Hoc Analysis of Organ Allocation …

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/framework_for_analysis_of_organ_allocation_policies.pdf
    15 Nov 2021: 12 Background. and their rapidity in test phase compared to competing kernel methods, the network is thenoptimized through gradient descent.
  8. Better Encoders for Neural Process Family Models

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/better_encoders_for_neural_process_family_models.pdf
    6 Dec 2022: representation can be learnt. However, once meta-training has been performed, the test-. ... having the true observations passed through to make realistic predictions at test-time.
  9. thesis

    https://www.mlmi.eng.cam.ac.uk/files/james_requeima_8224681_assignsubmission_file_requeimajamesthesis.pdf
    30 Oct 2019: 53. Chapter 1. Introduction. 1.1 Optimization. Optimization problems are widespread in science, engineering, economics and finance.For example, regional electricity grid system operators (ISOs) optimise the productionof electricity (solar, wind
  10. Interpretability for Conditional Average Treatment Effect Estimation

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/interpretability_for_conditional_average_treatment_effect_estimation_-_javier_abad.pdf
    24 Jan 2022: Introduction. Inferring the causal effect of interventions is a fundamental problem in many domains,including economics, education, and healthcare.
  11. Non-Gaussian Lévy Processes in Machine Learning

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/non-gaussian_levy_processes_in_machine_learning_reduced.pdf
    25 Nov 2022: Non-Gaussian Lévy Processes inMachine Learning. Trevor Clark. Machine Learning and Machine IntelligenceDepartment of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Fitzwilliam College
  12. Large Language Models for Reliable Information Extraction

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/large_language_models_for_reliable_information_extraction.pdf
    24 Nov 2023: This dataset provides agood setting to test the ability of LLMs to comprehend non-trivial information.
  13. Joint Learning of Practical Dialogue Systems and User Simulators

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/joint_learning_of_practical_dialogue_systems_and_user_simulators_reduced.pdf
    6 Dec 2022: US User Simulator. 3, 6. Chapter 1. Introduction. 1.1 Motivation. Language is a fundamentally important part of human intelligence, and is at the heart of many ofthe most important economic ... The full dataset contains approximately 10,400 dialogues,
  14. Mitigating Gender Bias in Dialogue Generation Gabrielle (Ming Yi) ...

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/mitigating_gender_bias_in_dialogue_generation.pdf
    15 Nov 2021: author noted that they cannot test the effectiveness of this method for gender bias because.
  15. Results that match 1 of 2 words

  16. Assessment | MPhil in Machine Learning and Machine Intelligence

    https://www.mlmi.eng.cam.ac.uk/course-structure/assessment
    14 Jul 2024: These include unseen written tests, take-home tests, reports, practical write-ups, presentations, essays, demonstrations, or other exercises.
  17. Frequently Asked Questions | MPhil in Machine Learning and Machine…

    https://www.mlmi.eng.cam.ac.uk/frequently-asked-questions
    14 Jul 2024: Q. Is a TOEFL test score sufficient for this programme or does each applicant need an IELTS test score?
  18. Academic Background | MPhil in Machine Learning and Machine…

    https://www.mlmi.eng.cam.ac.uk/how-apply/academic-background
    14 Jul 2024: A mathematically focussed Economics degree can sometimes be suitable preparation. Students will be expected to have strong backgrounds in mathematics and computer programming, as well as practical skills for large-scale
  19. Weight Uncertainty in Neural Networks

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_advanced_machine_learning_posters/weight_uncertainty_in_neural_networks_2.pdf
    14 Dec 2023: 50k/10k/10k data split, trained using SGD optimizer. Model # Units Test Error Test Error. ... Test error 1.58% 1.62% 1.75% 1.84%. Table 2. Classification error after weight pruning.
  20. Doubly Stochastic Variational Inference for Deep Gaussian Processes

    https://www.mlmi.eng.cam.ac.uk/files/doubly_stochastic_variational_inference_for_deep_gaussian_process.pdf
    7 Jul 2020: Nn=1 p(yn|fn) where inference over. test locations x is. f(x)|y GP (kff (Kff σ2yI)1y,Kff kff (Kff σ2yI)1kff. ... Regression. Figure: Regression test log-likelihood results on benchmark UCI datasets. The plots show the mean standarddeviation over 20
  21. Well-Calibrated Bayesian NeuralNetworks On the empirical assessment…

    https://www.mlmi.eng.cam.ac.uk/files/jheek_thesis.pdf
    6 Nov 2019: Otherwise it would be trivial to construct a failing calibration test forany model. ... Calibration tests could help alleviate thisissue which is otherwise inevitable for popular benchmarks.
  22. 3D Human Motion Synthesis with Recurrent Gaussian Processes

    https://www.mlmi.eng.cam.ac.uk/files/3d_human_motion_synthesis_with_recurrent_gaussian_processes_yeziwei_wang.pdf
    6 Nov 2019: For prediction, the initial 20 frames of the test sequence are fed to the trained model for motion generation. ... 4 Different motions will be explored to test howwell the model generalises.

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