5 citations · 6 across the 13 of their papers we have counts for
14 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-r…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
Probabilistic Retrofitting of Learned Simulators
Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2
Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertai…
Incremental Transformer Neural Processes
Philip Mortimer, Cristiana Diaconu, Tommy Rochussen +2
Neural Processes (NPs), and specifically Transformer Neural Processes (TNPs), have demonstrated remarkable performance across tasks ranging from spatiotemporal forecasting to tabul…
Use What You Know: Causal Foundation Models with Partial Graphs
Arik Reuter, Anish Dhir, Cristiana Diaconu +6
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified a…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu +3
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the ca…
On conditional diffusion models for PDE simulations
Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin +4
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 dat…
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data
Matthew Ashman, Cristiana Diaconu, Eric Langezaal +2
Many important problems require modelling large-scale spatio-temporal datasets, with one prevalent example being weather forecasting. Recently, transformer-based approaches have sh…
In-Context In-Context Learning with Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +1
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 da…
Translation Equivariant Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim +5
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved sinc…
Approximately Equivariant Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +2
Equivariant deep learning architectures exploit symmetries in learning problems to improve the sample efficiency of neural-network-based models and their ability to generalise. How…
Denoising Diffusion Probabilistic Models in Six Simple Steps
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou +3
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 imag…