Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Markou, Stratis"'
Autor:
Ashman, Matthew, Diaconu, Cristiana, Kim, Junhyuck, Sivaraya, Lakee, Markou, Stratis, Requeima, James, Bruinsma, Wessel P., Turner, Richard E.
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved since their inception. This improvement can be attributed to two principal
Externí odkaz:
http://arxiv.org/abs/2406.12409
Autor:
Räisä, Ossi, Markou, Stratis, Ashman, Matthew, Bruinsma, Wessel P., Tobaben, Marlon, Honkela, Antti, Turner, Richard E.
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the gold standard for protecting user privacy, standard DP mechanisms typica
Externí odkaz:
http://arxiv.org/abs/2406.08569
Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpin models as diverse as efficient transformers (by approximating attent
Externí odkaz:
http://arxiv.org/abs/2405.16541
Autor:
Vaughan, Anna, Markou, Stratis, Tebbutt, Will, Requeima, James, Bruinsma, Wessel P., Andersson, Tom R., Herzog, Michael, Lane, Nicholas D., Chantry, Matthew, Hosking, J. Scott, Turner, Richard E.
Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipel
Externí odkaz:
http://arxiv.org/abs/2404.00411
Autor:
Turner, Richard E., Diaconu, Cristiana-Diana, Markou, Stratis, Shysheya, Aliaksandra, Foong, Andrew Y. K., Mlodozeniec, Bruno
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather foreca
Externí odkaz:
http://arxiv.org/abs/2402.04384
Relative entropy coding (REC) algorithms encode a sample from a target distribution $Q$ using a proposal distribution $P$ using as few bits as possible. Unlike entropy coding, REC does not assume discrete distributions or require quantisation. As suc
Externí odkaz:
http://arxiv.org/abs/2309.15746
Autor:
Bruinsma, Wessel P., Markou, Stratis, Requiema, James, Foong, Andrew Y. K., Andersson, Tom R., Vaughan, Anna, Buonomo, Anthony, Hosking, J. Scott, Turner, Richard E.
Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable
Externí odkaz:
http://arxiv.org/abs/2303.14468
Autor:
Picheny, Victor, Berkeley, Joel, Moss, Henry B., Stojic, Hrvoje, Granta, Uri, Ober, Sebastian W., Artemev, Artem, Ghani, Khurram, Goodall, Alexander, Paleyes, Andrei, Vakili, Sattar, Pascual-Diaz, Sergio, Markou, Stratis, Qing, Jixiang, Loka, Nasrulloh R. B. S, Couckuyt, Ivo
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within sequential d
Externí odkaz:
http://arxiv.org/abs/2302.08436
Autor:
Andersson, Tom R., Bruinsma, Wessel P., Markou, Stratis, Requeima, James, Coca-Castro, Alejandro, Vaughan, Anna, Ellis, Anna-Louise, Lazzara, Matthew A., Jones, Dani, Hosking, J. Scott, Turner, Richard E.
Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like
Externí odkaz:
http://arxiv.org/abs/2211.10381
Autor:
Markou, Stratis
The challenge of simulating random variables is a central problem in Statistics and Machine Learning. Given a tractable proposal distribution $P$, from which we can draw exact samples, and a target distribution $Q$ which is absolutely continuous with
Externí odkaz:
http://arxiv.org/abs/2205.15250