Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Espeholt, Lasse"'
Autor:
Andrychowicz, Marcin, Espeholt, Lasse, Li, Di, Merchant, Samier, Merose, Alexander, Zyda, Fred, Agrawal, Shreya, Kalchbrenner, Nal
Deep neural networks offer an alternative paradigm for modeling weather conditions. 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 th
Externí odkaz:
http://arxiv.org/abs/2306.06079
Autor:
Espeholt, Lasse, Agrawal, Shreya, Sønderby, Casper, Kumar, Manoj, Heek, Jonathan, Bromberg, Carla, Gazen, Cenk, Hickey, Jason, Bell, Aaron, Kalchbrenner, Nal
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among others. Current operational forecasting models are based on physics a
Externí odkaz:
http://arxiv.org/abs/2111.07470
Autor:
Adolphs, Leonard, Boerschinger, Benjamin, Buck, Christian, Huebscher, Michelle Chen, Ciaramita, Massimiliano, Espeholt, Lasse, Hofmann, Thomas, Kilcher, Yannic, Rothe, Sascha, Sessa, Pier Giuseppe, Saralegui, Lierni Sestorain
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks. Our approach uses machine reading to guide the selection of refinement terms from aggregated
Externí odkaz:
http://arxiv.org/abs/2109.00527
Object-centric representations have recently enabled significant progress in tackling relational reasoning tasks. By building a strong object-centric inductive bias into neural architectures, recent efforts have improved generalization and data effic
Externí odkaz:
http://arxiv.org/abs/2104.09402
Autor:
Sønderby, Casper Kaae, Espeholt, Lasse, Heek, Jonathan, Dehghani, Mostafa, Oliver, Avital, Salimans, Tim, Agrawal, Shreya, Hickey, Jason, Kalchbrenner, Nal
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuously collected data and a rich spatial and temporal structure that presen
Externí odkaz:
http://arxiv.org/abs/2003.12140
We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the co
Externí odkaz:
http://arxiv.org/abs/1910.06591
Autor:
Kurach, Karol, Raichuk, Anton, Stańczyk, Piotr, Zając, Michał, Bachem, Olivier, Espeholt, Lasse, Riquelme, Carlos, Vincent, Damien, Michalski, Marcin, Bousquet, Olivier, Gelly, Sylvain
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. We introduce the Google Resear
Externí odkaz:
http://arxiv.org/abs/1907.11180
Autor:
Hessel, Matteo, Soyer, Hubert, Espeholt, Lasse, Czarnecki, Wojciech, Schmitt, Simon, van Hasselt, Hado
The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent
Externí odkaz:
http://arxiv.org/abs/1809.04474
Autor:
Espeholt, Lasse, Soyer, Hubert, Munos, Remi, Simonyan, Karen, Mnih, Volodymir, Ward, Tom, Doron, Yotam, Firoiu, Vlad, Harley, Tim, Dunning, Iain, Legg, Shane, Kavukcuoglu, Koray
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distri
Externí odkaz:
http://arxiv.org/abs/1802.01561
Autor:
Kalchbrenner, Nal, Espeholt, Lasse, Simonyan, Karen, Oord, Aaron van den, Graves, Alex, Kavukcuoglu, Koray
We present a novel neural network for processing sequences. 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. The two network pa
Externí odkaz:
http://arxiv.org/abs/1610.10099