Search

Search Funnelback University

Search powered by Funnelback
10 results 20 results 50 results
Relevance Date (Newest first) Date (Oldest first) Title (A-Z) Title (Z-A) Distance URL (A-Z) URL (Z-A) Shuffle
21 - 30 of 141 search results for TALK:Vb20 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. 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

  3. Breaking the Limits of Diffusion Models via Continuous Dynamical…

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/breaking_the_limits_of_diffusion_models.pdf
    14 Nov 2023: 24. 3.3 Illustration of our custom ODE block capturing continuous-time dynamics.The block processes outputs from the derivative function approximator anduses them to delineate the data’s evolutionary trajectory.

  4. Well-Calibrated Bayesian NeuralNetworks On the empirical assessment…

    https://www.mlmi.eng.cam.ac.uk/files/jheek_thesis.pdf
    6 Nov 2019: 𝜃)𝑞𝜙(𝜃) ]. (2.24). 5More generally, the argument that follows holds for any family of distributions 𝑞𝜙(𝜃) where the entropy𝔼[ log 𝑞𝜙(𝜃)] is invariant w.r.t. ... the global reparameterisation trick (2.23).Alternatively,

  5. Efficiently-Parametrised Approximate Posteriors in Pseudo-Point…

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/efficiently_parametrized_approximate_posteriors.pdf
    15 Nov 2021: exp(L1)p(u)q(u)q(u). du. (L1 log p(u)log q(u))q(u)du. = Eq(u) (L1 log p(u)log q(u)) L2 (2.24). ... L2 in Hensman et al. (2013) as follows (see equation 2.24 and 2.20 for the definition of L2and L1 respectively):.

  6. Contrastive Self-Supervised Learning for Tabular Data

    https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/contrastive_self-supervised_learning.pdf
    9 Dec 2021: Contrastive Self-Supervised Learningfor Tabular Data. Hugh Bishop. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine Learning and Machine Intelligence. Wolfson College

  7. Extending Deep GPs: Novel Variational Inference Schemes and a GPU…

    https://www.mlmi.eng.cam.ac.uk/files/maximilian_chamberlin_8224701_assignsubmission_file_mc.pdf
    30 Oct 2019: 24. Chapter 1. Introduction: The Deep GaussianProcess Model. 1.1 What are Deep GPs?

  8. 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: 24. xii Table of contents. 3.2.1 Frame-level KD in CTC. 243.2.2 Sequence-level KD in CTC. ... However, the definition of. 24 Knowledge Distillation in ASR. "knowledge" varies from area to area.

  9. Variable length word encodings forneural translation models Jiameng…

    https://www.mlmi.eng.cam.ac.uk/files/jiameng_gao_8224881_assignsubmission_file_j_gao_mphil_dissertation.pdf
    30 Oct 2019: the best MCR in Cambridge. I’ve absolutely loved 24 Parkside, everyone here had made. ... h, ,i (2.24). Where is a non-terminal symbol, while , 2 (X [ V) are a string of terminalsand non-terminals in the source and target languages respectively, where

  10. Graph Neural Stochastic Differential Equations

    https://www.mlmi.eng.cam.ac.uk/files/2022_-_2023_dissertations/graph_neural_stochastic_differential_equations.pdf
    17 Nov 2023: 2.3.6 Comparison: Oversmoothing in GN-ODE vs. Standard GNN. 24. 3 Graph Neural Stochastic Differential Equations 26. ... 24. 3.1 The left image illustrates the political compass of voters while the rightimage presents their social circles, with colors

  11. ALTA Project - Spoken Language Assessment and Learning

    https://www.mlmi.eng.cam.ac.uk/files/junjie_pan_8224791_assignsubmission_file_junjie_pan_dissertation_jp697.pdf
    30 Oct 2019: Its loss function is:. loss(H,Hre f ) = #(Corrections) #(Insertions) = A(H,Hre f ) (2.24). ... BLXXXtrn04 442 Spanish(100%) 26.53BLXXXeval1 223 Gujarati(100%) 24.11BLXXXeval2 220 Spanish(100%) 24.68. BLXXXeval3 226Dutch(14.6%), Polish(17.7%), French(16.4%

Refine your results

Date

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.