Zobrazeno 1 - 10
of 288
pro vyhledávání: '"Turner, Richard E."'
Autor:
Shysheya, Aliaksandra, Diaconu, Cristiana, Bergamin, Federico, Perdikaris, Paris, Hernández-Lobato, José Miguel, Turner, Richard E., Mathieu, Emile
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as data assimilation. However, most previous numerical and machine learning
Externí odkaz:
http://arxiv.org/abs/2410.16415
Many important problems require modelling large-scale spatio-temporal datasets, with one prevalent example being weather forecasting. Recently, transformer-based approaches have shown great promise in a range of weather forecasting problems. However,
Externí odkaz:
http://arxiv.org/abs/2410.06731
Autor:
Reid, Isaac, Dubey, Kumar Avinava, Jain, Deepali, Whitney, Will, Ahmed, Amr, Ainslie, Joshua, Bewley, Alex, Jacob, Mithun, Mehta, Aranyak, Rendleman, David, Schenck, Connor, Turner, Richard E., Wagner, René, Weller, Adrian, Choromanski, Krzysztof
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relative position encoding, achieves this by upweighting or downweighting att
Externí odkaz:
http://arxiv.org/abs/2410.03462
Autor:
Vaughan, Anna, Mateo-Garcia, Gonzalo, Irakulis-Loitxate, Itziar, Watine, Marc, Fernandez-Poblaciones, Pablo, Turner, Richard E., Requeima, James, Gorroño, Javier, Randles, Cynthia, Caltagirone, Manfredi, Cifarelli, Claudio
Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise. Despite the demonstrated ability of remote sensing instruments to detect methane plumes, no system has been available to ro
Externí odkaz:
http://arxiv.org/abs/2408.04745
Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. However, existing open-source VLMs heavily rely on pretrained and frozen
Externí odkaz:
http://arxiv.org/abs/2407.16526
Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each dataset in a meta-dataset is sampled. There are many cases in which prac
Externí odkaz:
http://arxiv.org/abs/2406.13493
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. However, when modelling real-world data, learning problems are often not
Externí odkaz:
http://arxiv.org/abs/2406.13488
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:
Cohen, Yarden, Navarro, Alexandre Khae Wu, Frellsen, Jes, Turner, Richard E., Riemer, Raziel, Pakman, Ari
The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circle-valued random functions related to Gaussian processes targeting two
Externí odkaz:
http://arxiv.org/abs/2406.13151
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