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1 - 10 of 28 search results for Economics test |u:www.mlmi.eng.cam.ac.uk where 2 match all words and 26 match some words.

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  2. 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.

  3. 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.

  4. Results that match 1 of 2 words

  5. The University of Cambridge, Advanced Machine Learning Conditional…

    https://www.mlmi.eng.cam.ac.uk/files/conditional_neural_processes.pdf
    1 Feb 2021: Pixel-wise image regression on MNISTFor this task we test the CNPs on the MNIST dataset. ... Music Completion on MIDIWe test CNP architecture on the MAESTRO dataset [2], containing 200hof piano music.

  6. AML - Poster

    https://www.mlmi.eng.cam.ac.uk/files/weight_uncertainty_in_neural_networks.pdf
    1 Feb 2021: Weights Removed (%) No. of Active Weights Test Error Rate (%)0 478410 2.5950 239205 2.5275 119603 2.6295 23921 2.7598 9569 3.14. •

  7. Improving Deep Ensembles for Better Deep Uncertainty Quantification

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/improving_deep_ensembles.pdf
    15 Nov 2021: Test-time predictions are obtained by. 2.2 The Uncertainty Calibration Problem 7. ... 1.0. Accu. racy. ResNet-20 on the CIFAR10 Test SetIdeal calibrationMean accuracy forconfidence interval.

  8. Interpretable Machine Learning

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/interpretable_machine_learning.pdf
    11 Feb 2021: techniques b) unlike all of the other methods, it is incredibly fast during test time(solving symbolic regression corresponds to a single forward pass in the network). ... wings, feathers, beaks, and determine what iscausing the problem. In addition, we

  9. Bootstrap Your Flow

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/bootstrap_your_flow_reduced.pdf
    15 Nov 2021: 72. B.2 32 dimensional Many Well test-set performance during training. 73B.3 32 dimensional Many Well Marginals. ... We test FAB on a 2-dimensional mixture of Gaussians (MoG) problem, allowing visual inspection of the trainingprocess.

  10. Knowledge Distillation for End-to-End Automatic Speech Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/knowledge_distillation_for_end-to-end_asr.pdf
    9 Dec 2021: clean and test-other sets, suggesting a promising approach ofcombining knowledge distillation with semi-supervised learning to improve ASR models. ... By mimicking the output behaviourof the large model, the student model might achieve even better

  11. Multimodal Emotion Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/multimodal_emotion_recognition.pdf
    11 Feb 2021: 234.5 Basic configuration test of the number of heads of attentive later. ... Table 4.5 Basic configuration test of the number of heads of attentive layer.

  12. Depth Uncertainty Networks for Active Learning

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/depth_uncertainty_networks_for_active_learning_reduced.pdf
    15 Nov 2021: This thesis proposes and tests several hypotheses about the performance of DUNs in activelearning settings. ... 344.2 Test NLL vs. number of training points for DUNs evaluated on toy datasets.

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