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1 - 10 of 13 search results for KaKaoTalk:PC53 24 / |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 13 match some words.

  1. Results that match 1 of 2 words

  2. Utilizing Large Language Models for Question Answering in…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/large_language_models_for_question_answering.pdf
    23 Nov 2023: 24. 5.1 Replication of RAG results for Task II. 385.2 Shared Task Evaluation for generator-only Chat Completion versions.

  3. Incorporating Vision Encoders into Retrieval Augmented Visual…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/visual_question_answering_li.pdf
    24 Nov 2023: 243.3.2 In-context Few-shot Prompting. 24. 3.4 Conclusion. 25. Table of contents v.

  4. Optimal PAC-Bayes Bounds and their Variational Approximations

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/optimal_pac-bayes_bounds.pdf
    24 Nov 2023: Westate this lower bound next. 24 Method. 3.2 A Lower Bound on the Tightness Gap.

  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: 23. 3.4 Summary. 24. 4 Results and Discussions 254.1 In-context Learning for NMT. ... The loss function can be any string-to-string distance function, or the negative of string-to-string alignment function such as BLEU [24] and BLEURT [34] (see Section

  6. Improving Uncertainty Quantification in Regression Problems through…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/improving_uncertainty_quantification_in_regression_problems.pdf
    23 Nov 2023: Improving UncertaintyQuantification in RegressionProblems through Conformal. Training. Johannes Vallikivi. MPhil in Machine Learning and Machine IntelligenceDepartment of Engineering. University of Cambridge. This dissertation is submitted for the

  7. Evaluating Benefits of Heterogeneity in Constrained Multi-Agent…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/evaluating_benefits_of_heterogeneity.pdf
    14 Dec 2023: 22. 3.3 General Problem Formulation. 233.4 Training Pipeline. 24. 4 Environment Conditions and Diversity 274.1 Introducing the Left-Right Scenario.

  8. Pre-training Meta-models for Interpretability

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/pre-training_meta-models_for_interpretability.pdf
    23 Nov 2023: 24. Table of contents vii. 3.4.1 Task definition. 243.4.2 Model Zoo Generation.

  9. Sim2Real With Neural Processes Jonas Scholz Department of Engineering …

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/sim2real_with_neural_processes.pdf
    24 Nov 2023: They are lightweight,consisting of only 2 parameters per feature map. 16, 19, 21, 22, 24, 26–28, 38,41, 42, 56. ... set.3I.e. we covered learning rates of 1105, 3105, 1104,. 24. CHAPTER 4.

  10. Adapting Pretrained Vision-Language Models in Medical Domains

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/adapting_pretrained_vision-language_models.pdf
    28 Nov 2023: Adapting Pretrained Vision-LanguageModels in Medical Domains. Liangchen Li. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Clare

  11. Incorporating Vision Encoders into Retrieval Augmented Visual…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/visual_question_answering_nikolic.pdf
    24 Nov 2023: Incorporating Vision Encoders intoRetrieval Augmented Visual Question. Answering. Kristina Nikolić. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and

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