Probabilistic weather forecasting is critical for decision-making in high-impact domains such as flood forecasting, energy system planning or transportation routing, where quantifying the uncertainty of a forecast -- including probabilities of extreme events -- is essential to guide important cost-benefit trade-offs and mitigation measures.
Framing inference as the inner-loop optimization of meta-learning leads to a model-based approach that is more data-efficient and capable of estimating the state of entities that we do not observe directly, but whose existence can be inferred from their effect on observed entities.
4 code implementations • 24 Dec 2022 • Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, Peter Battaglia
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases.
Program induction lies at the opposite end of the spectrum: programs are capable of extrapolating from very few examples, but we still do not know how to efficiently search for complex programs.
Adding auxiliary losses to the main objective function is a general way of encoding biases that can help networks learn better representations.
We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime.
Such models, however, are approximate, which limits their applicability.
We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure.
Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.
Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning.
In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections.
3 code implementations • 3 Oct 2017 • Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R. Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, Nima Fazeli, Ferran Alet, Nikhil Chavan Dafle, Rachel Holladay, Isabella Morona, Prem Qu Nair, Druck Green, Ian Taylor, Weber Liu, Thomas Funkhouser, Alberto Rodriguez
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.