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Found 21 papers, 12 papers with code
JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
We introduce a simple method for probabilistic predictions on tabular data based on Large Language Models (LLMs) called JoLT (Joint LLM Process for Tabular data).
A Meta-Learning Approach to Bayesian Causal Discovery
To address these limitations, we propose a Bayesian meta learning model that allows for sampling causal structures from the posterior and encodes these key properties.
Context is Key: A Benchmark for Forecasting with Essential Textual Information
To address this, we introduce "Context is Key" (CiK), a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities.
AI for operational methane emitter monitoring from space
Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise.
Translation Equivariant Transformer Neural Processes
Notably, the posterior prediction maps for data that are stationary -- a common assumption in spatio-temporal modelling -- exhibit translation equivariance.
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language
Machine learning practitioners often face significant challenges in formally integrating their prior knowledge and beliefs into predictive models, limiting the potential for nuanced and context-aware analyses.
Aardvark weather: end-to-end data-driven weather forecasting
Local station forecasts are skillful up to ten days lead time and achieve comparable and often lower errors than a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters.
Diffusion-Augmented Neural Processes
Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable.
Sim2Real for Environmental Neural Processes
On held-out weather stations, Sim2Real training substantially outperforms the same model architecture trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations.
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
Challenges and Pitfalls of Bayesian Unlearning
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model.
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.
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.
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a, b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines.
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.
Ranked #6 on
Few-Shot Image Classification
on Meta-Dataset Rank
The Gaussian Process Autoregressive Regression Model (GPAR)
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance.
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
These results show that PDTS is a successful solution for large-scale parallel BO.
