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1 - 10 of 13 search results for tj KaKaotalk:PC53 |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. Bootstrap Your Flow

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/bootstrap_your_flow_reduced.pdf
    15 Nov 2021: p̃ j(x) = p̃N(x)β j p0(x)1β j (2.23). where 0 = β0 < β1 <. < βN = 1. We require that the Markov chain transitions Tj mustleave the corresponding p ... We do not require Tj satisfies the typicalMCMC requirement for ergodicity, however this is

  3. 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: ŷj = y(C)(tj) =. NCi=1. ϕ (yCi)ψE (tj xCi) (2.7). Here ψE is the encoder basis function, which is chosen to be a squared-exponentialkernel of lengthscale E:.

  4. mphilthesis.def

    https://www.mlmi.eng.cam.ac.uk/files/graziani_dissertation.pdf
    30 Oct 2019: Improved Interpretability andGeneralisation for Deep Learning. Mara Graziani. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy. Pembroke College August 2017. To who can think, who

  5. thesis

    https://www.mlmi.eng.cam.ac.uk/files/burt_thesis.pdf
    6 Nov 2019: This structure allows us to extrapolate or interpolate information about a collec-tion of random variables { f (xi)}Ni=1 to make predictions at new input values { f (xj)}Tj=1.

  6. Multimodal Emotion Recognition

    https://www.mlmi.eng.cam.ac.uk/files/2019-2020_dissertations/multimodal_emotion_recognition.pdf
    11 Feb 2021: pi =eW. Tyi. xibyi. j eW Tj xib j. (3.6). where xi, Wj, Wyi are the i-th training sample, the j-th and yi-th column of fully connectedlayer ... Li = log. (eW. Tyi. xibyi. j eW Tj xib j.

  7. acs-dissertation

    https://www.mlmi.eng.cam.ac.uk/files/konstantinos_tsakalis_8224911_assignsubmission_file_dissertation_signed.pdf
    30 Oct 2019: transcription Tj. and is accompanied with a score from the language model.

  8. Establishing a Unified Framework for Iterative Machine Teaching

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/framework_for_iterative_machine_teaching.pdf
    17 Nov 2023: wt = wt1 ηt 1m. m. j=1. l(xtj,y. tj|wt1. ) wt1.

  9. 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: dTWass-RD(i, j) = W2(rTi ,r. Tj ) (3.10). This modified measure is useful to understand the action distance between agents, but it isstill limited by the fact that it

  10. Understanding and Fixing the Modality Gap in Vision-Language Models

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/understanding_and_fixing_the_modality_gap_in_vision-language_models_reduced.pdf
    25 Nov 2022: i=1. logexp(⟨Ti,Ii⟩/τ). Nj=1 exp(⟨Ti,I j⟩/τ). LIT =1N. N. i=1. logexp(⟨Ii,Ti⟩/τ). Nj=1 exp(⟨Ii,Tj⟩/τ).

  11. Autoregressive Conditional Neural Processes

    https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/autoregressive_conditional_neural_processes_reduced.pdf
    25 Nov 2022: Both sets consists of input, outputs pairs DC = {x j,y j}Cj=1 andDT = {x j,y j}Tj=1, where C, and T are the cardinalities of DC and

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