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1 - 3 of 3 search results for tj KaKaotalk:PC53 |u:www.mlmi.eng.cam.ac.uk where 0 match all words and 3 match some words.

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  2. 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).

  3. 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

  4. Neural ProcessesGinte Petrulionyte Yuriko Kobe Jack Davis Neural…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_advanced_machine_learning_posters/neural_processes_2021.pdf
    25 Jan 2022: Neural ProcessesGinte Petrulionyte Yuriko Kobe Jack Davis. Neural networks (NNs) are effective function approximators, but do not captureuncertainty over their predictions and cannot easily be updated after training. Gaussian Processes (GPs) are

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