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Found 9 papers, 4 papers with code
Aardvark Weather: end-to-end data-driven weather forecasting
Machine learning is revolutionising medium-range weather prediction.
Denoising Diffusion Probabilistic Models in Six Simple Steps
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 forecasting, and neural surrogates of partial differential equations.
Autoregressive Conditional Neural Processes
Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
Practical Conditional Neural Process Via Tractable Dependent Predictions
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
Efficient Gaussian Neural Processes for Regression
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test time, and are trainable via a simple maximum likelihood procedure.
