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1 - 10 of 17 search results for Psychology |u:www.mlmi.eng.cam.ac.uk

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  2. Improving Sample Efficiency forGradient-based Policy Optimisation;…

    https://www.mlmi.eng.cam.ac.uk/files/wang_dissertation.pdf
    30 Oct 2019: Inspired by this kind ofbehaviorist psychology, the field of Reinforcement Learning (RL) emerges as a frameworkfor solving sequential decision-making problems through interaction with the environment.[Sutton and Barto, 1998] provides

  3. Curiosity-Driven Reinforcement Learning for Dialogue Management

    https://www.mlmi.eng.cam.ac.uk/files/paulawesselmann_mlsalt.pdf
    6 Nov 2019: 2.3.1 Optimal Challenge. Psychology suggests that curiosity or interest is only "engaged by what is just beyond currentknowledge, neither too well known nor too far beyond what is understandable"

  4. Combining Diverse Neural Network Language Models for Speech…

    https://www.mlmi.eng.cam.ac.uk/files/xianrui_zheng.pdf
    18 Nov 2019: Modern technologies for analysingspeech require knowledge from fields including but not limited to linguistics, engineeringand psychology.

  5. A model-based design tool for 3D GUI layout design that accommodates…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/a_model-based_design_tool.pdf
    15 Nov 2021: The ‘optimality’ of these layouts is often variable to human perception,psychology, and preference; an optimal design for one user will usually not be optimal for allother users. ... designknowledge. The key challenge with designing an optimal UI is

  6. Fair Policy Learning

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/fair_policy_learning.pdf
    24 Jan 2022: Fair Policy Learning. Tennison Liu. Supervisor: Prof. Mihaela van der. Schaar. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. St

  7. Compression without Quantization Gergely Flamich Department of…

    https://www.mlmi.eng.cam.ac.uk/files/compression_without_quantization_flamich_reduced.pdf
    18 Nov 2019: from several otherdisciplines, such as mathematics, neuroscience, psychology and photography.

  8. Stochastic Memory for Sequence Models Making Good Compressors Use ...

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/stochastic_memory_for_sequence_models.pdf
    2 Mar 2023: Stochastic Memory for Sequence Models. Making Good Compressors Use Less Memory. David Michael Goldfarb. Supervisor: Dr. Christian Steinruecken. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster

  9. 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: Beyond independent maskingin tabular self-supervision. Stuart Andrew Burrell. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Corpus Christi College September 2022. Declaration.

  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: Knowledge Distillation for End-to-EndAutomatic Speech Recognition. Xiaoyu Yang. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence.

  11. BachBot: Automatic composition in thestyle of Bach chorales…

    https://www.mlmi.eng.cam.ac.uk/files/feynman_liang_8224771_assignsubmission_file_liangfeynmanthesis.pdf
    30 Oct 2019: BachBot: Automatic composition in thestyle of Bach chorales. Developing, analyzing, and evaluating a deep LSTM modelfor musical style. Feynman Liang. Department of EngineeringUniversity of Cambridge. M.Phil in Machine Learning, Speech, and Language

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