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Found 13 papers, 7 papers with code
GenCast: Diffusion-based ensemble forecasting for medium-range weather
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.
Neural Relational Inference with Fast Modular Meta-learning
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.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
Noether Networks: Meta-Learning Useful Conserved Quantities
Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases.
Measuring few-shot extrapolation with program induction
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.
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Adding auxiliary losses to the main objective function is a general way of encoding biases that can help networks learn better representations.
Meta-learning curiosity algorithms
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.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Such models, however, are approximate, which limits their applicability.
Graph Element Networks: adaptive, structured computation and memory
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
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
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
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.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
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.
