Search Results for author: Ferran Alet

Found 13 papers, 7 papers with code

GenCast: Diffusion-based ensemble forecasting for medium-range weather

no code implementations25 Dec 2023 Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, Matthew Willson

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.

Decision Making Weather Forecasting

Neural Relational Inference with Fast Modular Meta-learning

1 code implementation NeurIPS 2019 Ferran Alet, Erica Weng, Tomás Lozano Pérez, Leslie Pack Kaelbling

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.


Noether Networks: Meta-Learning Useful Conserved Quantities

no code implementations NeurIPS 2021 Ferran Alet, Dylan Doblar, Allan Zhou, Joshua Tenenbaum, Kenji Kawaguchi, Chelsea Finn

Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases.

Meta-Learning Translation

Measuring few-shot extrapolation with program induction

no code implementations NeurIPS Workshop CAP 2020 Ferran Alet, Javier Lopez-Contreras, Joshua B. Tenenbaum, Tomas Perez, Leslie Pack Kaelbling

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.

Meta-Learning Program induction

Meta-learning curiosity algorithms

1 code implementation ICLR 2020 Ferran Alet, Martin F. Schneider, Tomas Lozano-Perez, Leslie Pack Kaelbling

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.

Acrobot Meta-Learning

Graph Element Networks: adaptive, structured computation and memory

2 code implementations18 Apr 2019 Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling

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 in abstract graph networks for combinatorial generalization

1 code implementation19 Dec 2018 Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie P. Kaelbling

Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.


Modular meta-learning

1 code implementation26 Jun 2018 Ferran Alet, Tomás Lozano-Pérez, Leslie P. Kaelbling

Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning.


Finding Frequent Entities in Continuous Data

no code implementations8 May 2018 Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling, Tomas Lozano-Perez

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.


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