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Neural ProcessesGinte Petrulionyte Yuriko Kobe Jack Davis NeuralâŚ
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_advanced_machine_learning_posters/neural_processes_2021.pdf25 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 -
Exploration and Exploitation:From Bandits to Bayesian Optimisation
https://www.mlmi.eng.cam.ac.uk/files/2020-2021_dissertations/exploration_and_exploitation.pdf24 Jan 2022: Exploration and Exploitation:From Bandits to Bayesian Optimisation. Eli Persky. Supervisor: Prof. C. E. Rasmussen. Department of EngineeringUniversity of Cambridge. This dissertation is submitted for the degree ofMaster of Philosophy in Machine -
importance-weighted-autoencoders-poster (1)
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_advanced_machine_learning_posters/importance_weighted_autoencoders_poster_1_2022.pdf17 May 2022: Importance Weighted AutoencodersFederico Barbero, Kaiqu Liang, Haoran Peng. đ Generative model capable of learning latent representations from data z x. Architecture. Density Estimation. 1.Kingma, D. P., and Welling M., âAuto-encoding -
Scalable Bayesian Inference for Probabilistic Spectrotemporal ModelsâŚ
https://www.mlmi.eng.cam.ac.uk/files/2021-2022_dissertations/scalable_bayesian_inference_for_probabilistic_spectrotemporal_models.pdf5 Dec 2022: Intrinsic Residual. AsV<latexit -
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.pdf25 Nov 2022: Op-. timalâ refers to the loss values computed by using the optimal text points whereasâAlignedâ refers to the loss values computed by exactly aligning the text and imagepoints.
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