Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Wischik, Damon"'
Autor:
Parthipan, Raghul, Anand, Mohit, Christensen, Hannah M., Hosking, J. Scott, Wischik, Damon J.
Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulation in their forecasts is a key problem. However, there is no clear def
Externí odkaz:
http://arxiv.org/abs/2405.14714
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The intuition behind such a criterion is that, the pretrained LLM has the prior knowledg
Externí odkaz:
http://arxiv.org/abs/2404.08679
Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods
Externí odkaz:
http://arxiv.org/abs/2402.14169
The travel demand forecasting model plays a crucial role in evaluating large-scale infrastructure projects, such as the construction of new roads or transit lines. While combined modeling approaches have been explored as a solution to overcome the pr
Externí odkaz:
http://arxiv.org/abs/2308.01817
We propose a probabilistic perspective on adversarial examples, allowing us to embed subjective understanding of semantics as a distribution into the process of generating adversarial examples, in a principled manner. Despite significant pixel-level
Externí odkaz:
http://arxiv.org/abs/2306.00353
We propose the Taylorformer for random processes such as time series. Its two key components are: 1) the LocalTaylor wrapper which adapts Taylor approximations (used in dynamical systems) for use in neural network-based probabilistic models, and 2) t
Externí odkaz:
http://arxiv.org/abs/2305.19141
Autor:
Zhang, Andi, Wischik, Damon
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD. But this approach has been found to fail, in deep learning settings. In t
Externí odkaz:
http://arxiv.org/abs/2210.12767
Autor:
Parthipan, Raghul, Wischik, Damon J.
How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises coarse-grained high-resolution data. But only keeping this coarse-graine
Externí odkaz:
http://arxiv.org/abs/2210.04001
Autor:
Grant, Thomas D., Wischik, Damon J.
This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the pat
Externí odkaz:
http://library.oapen.org/handle/20.500.12657/39543
The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Red noise is essential to many operational parameterization
Externí odkaz:
http://arxiv.org/abs/2203.14814