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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-. timal’ refers to the loss values computed by using the optimal text points whereas‘Aligned’ refers to the loss values computed by exactly aligning the text and imagepoints. -
Scalable Bayesian Inference for Probabilistic Spectrotemporal Models…
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 -
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. 📖 Generative model capable of learning latent representations from data z x. Architecture. Density Estimation. 1.Kingma, D. P., and Welling M., ”Auto-encoding -
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 -
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.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
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