Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Ibarz, Borja"'
Autor:
Bounsi, Wilfried, Ibarz, Borja, Dudzik, Andrew, Hamrick, Jessica B., Markeeva, Larisa, Vitvitskyi, Alex, Pascanu, Razvan, Veličković, Petar
Transformers have revolutionized machine learning with their simple yet effective architecture. Pre-training Transformers on massive text datasets from the Internet has led to unmatched generalization for natural language understanding (NLU) tasks. H
Externí odkaz:
http://arxiv.org/abs/2406.09308
Autor:
Markeeva, Larisa, McLeish, Sean, Ibarz, Borja, Bounsi, Wilfried, Kozlova, Olga, Vitvitskyi, Alex, Blundell, Charles, Goldstein, Tom, Schwarzschild, Avi, Veličković, Petar
Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning focus on out-of-distribution performance on procedurally-generated synthe
Externí odkaz:
http://arxiv.org/abs/2406.04229
We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinat
Externí odkaz:
http://arxiv.org/abs/2305.13233
Autor:
Bevilacqua, Beatrice, Nikiforou, Kyriacos, Ibarz, Borja, Bica, Ioana, Paganini, Michela, Blundell, Charles, Mitrovic, Jovana, Veličković, Petar
Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of neural networks, effectively demonstrating they can learn to execute classical algorithms on unseen data coming from the train distribution. However, the perfo
Externí odkaz:
http://arxiv.org/abs/2302.10258
Autor:
Ibarz, Borja, Kurin, Vitaly, Papamakarios, George, Nikiforou, Kyriacos, Bennani, Mehdi, Csordás, Róbert, Dudzik, Andrew, Bošnjak, Matko, Vitvitskyi, Alex, Rubanova, Yulia, Deac, Andreea, Bevilacqua, Beatrice, Ganin, Yaroslav, Blundell, Charles, Veličković, Petar
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused
Externí odkaz:
http://arxiv.org/abs/2209.11142
Autor:
Wirnsberger, Peter, Papamakarios, George, Ibarz, Borja, Racanière, Sébastien, Ballard, Andrew J., Pritzel, Alexander, Blundell, Charles
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Hel
Externí odkaz:
http://arxiv.org/abs/2111.08696
To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning f
Externí odkaz:
http://arxiv.org/abs/1811.06521
Autor:
Kossio, Felipe Yaroslav Kalle, Goedeke, Sven, Akker, Benjamin van den, Ibarz, Borja, Memmesheimer, Raoul-Martin
Publikováno v:
Phys. Rev. Lett. 121(5), 058301, 2018
Experiments in various neural systems found avalanches: bursts of activity with characteristics typical for critical dynamics. A possible explanation for their occurrence is an underlying network that self-organizes into a critical state. We propose
Externí odkaz:
http://arxiv.org/abs/1811.02861
We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal (NPT) ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08bd8784ea196c4fb60e5e03da196a3d
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