Search Results for author: Lasse Espeholt

Found 12 papers, 9 papers with code

Deep Learning for Day Forecasts from Sparse Observations

no code implementations6 Jun 2023 Marcin Andrychowicz, Lasse Espeholt, Di Li, Samier Merchant, Alexander Merose, Fred Zyda, Shreya Agrawal, Nal Kalchbrenner

The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages.

Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks

2 code implementations14 Nov 2021 Lasse Espeholt, Shreya Agrawal, Casper Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, Nal Kalchbrenner

An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.

energy management Management +2

Agent-Centric Representations for Multi-Agent Reinforcement Learning

no code implementations19 Apr 2021 Wenling Shang, Lasse Espeholt, Anton Raichuk, Tim Salimans

Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.

Inductive Bias Multi-agent Reinforcement Learning +5

Google Research Football: A Novel Reinforcement Learning Environment

1 code implementation25 Jul 2019 Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.

Game of Football reinforcement-learning +1

Multi-task Deep Reinforcement Learning with PopArt

2 code implementations12 Sep 2018 Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt

This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.

Atari Games Multi-Task Learning +2

Neural Machine Translation in Linear Time

11 code implementations31 Oct 2016 Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, Koray Kavukcuoglu

The ByteNet is a one-dimensional convolutional neural network that is composed of two parts, one to encode the source sequence and the other to decode the target sequence.

Decoder Language Modelling +3

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