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1 - 10 of 141 search results for KaKaoTalk:ZA31 24 24 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 141 match some words.

  1. Results that match 1 of 2 words

  2. Poster Print Size:This poster template is 24” high by ...

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_advanced_machine_learning_posters/importance_weighted_encoder.pdf
    21 Jan 2022: Poster Print Size:This poster template is 24” high by 36” wide.

  3. Building a Conversational User Simulator Using Generative Adversarial …

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/building_a_conversational_user_simulator.pdf
    15 Nov 2021: 244.3.1 Policy Training. 244.3.2 Policy Evaluation. 24. 5 Adversarial Training Experiments 265.1 MLE Pre-Training.

  4. Practical bayesian optimization of machine learning algorithms…

    https://www.mlmi.eng.cam.ac.uk/files/practical_bayesian_optimization.pdf
    1 Feb 2021: In: In Advances in Neural Information Processing Systems 24. 2010,pp. 1723–1731.

  5. Understanding the properties of sparse Gaussian Processapproximations …

    https://www.mlmi.eng.cam.ac.uk/files/tebbutt_will_industry_day_poster.pdf
    30 Oct 2019: blue=full GP, red=sparse approx.).(Left: 24 pseudo-data. Right: 20 pseudo data.). Despite a small change in the number of pseudo-data, a qualitativechange in the approximation is observed.

  6. Pathologies of Deep Sparse Gaussian Process Regression

    https://www.mlmi.eng.cam.ac.uk/files/diaz_thesis.pdf
    30 Oct 2019: 22. 4.2.1 Pathological behaviour. 24. 4.3 Conclusion. 24. 5 Initialisation Schemes 27. ... p(ŷ|x̂, D, α) =. p(ŷ|f, x̂)p(f |D, α)df (2.24). 1M. Mm=1.

  7. thesis

    https://www.mlmi.eng.cam.ac.uk/files/burt_thesis.pdf
    6 Nov 2019: 23. 3.3.1 Covariances. 243.3.2 Cross covariances. 243.3.3 Eigenfunction based inducing points and the mean field approximation 24. ... Thefirst term in (2.24) can be thought of as an approximate marginal likelihood and the secondterm is a regularization

  8. Overcoming Catastrophic Forgetting in Neural Machine Translation

    https://www.mlmi.eng.cam.ac.uk/files/kell_thesis.pdf
    6 Nov 2019: 24. 5.1 Optimised λ , where the rows are the tasks and the columns are the models. ... 24 Weighted Interpolation. the score decreases as the weights are changed to favour the health-only model.

  9. 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: 23. 3.6 Token counts of GB-Ctrl validation data. 24. 3.7 Toxicity in GB-Ctrl finetuning data. ... 24. 3.8 Size of GBS-Ctrl finetuning datasets. 26. 3.9 StereoSet examples.

  10. Combining Diverse Neural Network Language Models for Speech…

    https://www.mlmi.eng.cam.ac.uk/files/xianrui_zheng.pdf
    18 Nov 2019: 24. 4 Pre-trained Language Models 254.1 GPT. 254.2 Transformer XL. 264.3 BERT. ... Loss(θ ) = CE(θ )λ. 2N θθ 2 (3.12). 24 Neural Network Language Models.

  11. Neural Network Compression

    https://www.mlmi.eng.cam.ac.uk/files/okz21_thesisfinal.pdf
    6 Nov 2019: 213.2 Independent Compression. 24. 4.1 Experimental Setup. 254.2 Image Examples from the MNIST Database. ... soft-weight sharing[24], and modifies it with the aim of further improving compression.

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