Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Glorot, Xavier"'
Autor:
Aygün, Eser, Orseau, Laurent, Anand, Ankit, Glorot, Xavier, Firoiu, Vlad, Zhang, Lei M., Precup, Doina, Mourad, Shibl
Traditional automated theorem provers for first-order logic depend on speed-optimized search and many handcrafted heuristics that are designed to work best over a wide range of domains. Machine learning approaches in literature either depend on these
Externí odkaz:
http://arxiv.org/abs/2112.10664
Autor:
Firoiu, Vlad, Aygun, Eser, Anand, Ankit, Ahmed, Zafarali, Glorot, Xavier, Orseau, Laurent, Zhang, Lei, Precup, Doina, Mourad, Shibl
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on trainin
Externí odkaz:
http://arxiv.org/abs/2103.03798
Autor:
Aygün, Eser, Ahmed, Zafarali, Anand, Ankit, Firoiu, Vlad, Glorot, Xavier, Orseau, Laurent, Precup, Doina, Mourad, Shibl
A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on trainin
Externí odkaz:
http://arxiv.org/abs/2006.11259
Autor:
Glorot, Xavier
En apprentissage automatique, domaine qui consiste à utiliser des données pour apprendre une solution aux problèmes que nous voulons confier à la machine, le modèle des Réseaux de Neurones Artificiels (ANN) est un outil précieux. Il a été in
Externí odkaz:
http://hdl.handle.net/1866/11989
Autor:
Higgins, Irina, Matthey, Loic, Glorot, Xavier, Pal, Arka, Uria, Benigno, Blundell, Charles, Mohamed, Shakir, Lerchner, Alexander
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation.
Externí odkaz:
http://arxiv.org/abs/1606.05579
Autor:
The Theano Development Team, Al-Rfou, Rami, Alain, Guillaume, Almahairi, Amjad, Angermueller, Christof, Bahdanau, Dzmitry, Ballas, Nicolas, Bastien, Frédéric, Bayer, Justin, Belikov, Anatoly, Belopolsky, Alexander, Bengio, Yoshua, Bergeron, Arnaud, Bergstra, James, Bisson, Valentin, Snyder, Josh Bleecher, Bouchard, Nicolas, Boulanger-Lewandowski, Nicolas, Bouthillier, Xavier, de Brébisson, Alexandre, Breuleux, Olivier, Carrier, Pierre-Luc, Cho, Kyunghyun, Chorowski, Jan, Christiano, Paul, Cooijmans, Tim, Côté, Marc-Alexandre, Côté, Myriam, Courville, Aaron, Dauphin, Yann N., Delalleau, Olivier, Demouth, Julien, Desjardins, Guillaume, Dieleman, Sander, Dinh, Laurent, Ducoffe, Mélanie, Dumoulin, Vincent, Kahou, Samira Ebrahimi, Erhan, Dumitru, Fan, Ziye, Firat, Orhan, Germain, Mathieu, Glorot, Xavier, Goodfellow, Ian, Graham, Matt, Gulcehre, Caglar, Hamel, Philippe, Harlouchet, Iban, Heng, Jean-Philippe, Hidasi, Balázs, Honari, Sina, Jain, Arjun, Jean, Sébastien, Jia, Kai, Korobov, Mikhail, Kulkarni, Vivek, Lamb, Alex, Lamblin, Pascal, Larsen, Eric, Laurent, César, Lee, Sean, Lefrancois, Simon, Lemieux, Simon, Léonard, Nicholas, Lin, Zhouhan, Livezey, Jesse A., Lorenz, Cory, Lowin, Jeremiah, Ma, Qianli, Manzagol, Pierre-Antoine, Mastropietro, Olivier, McGibbon, Robert T., Memisevic, Roland, van Merriënboer, Bart, Michalski, Vincent, Mirza, Mehdi, Orlandi, Alberto, Pal, Christopher, Pascanu, Razvan, Pezeshki, Mohammad, Raffel, Colin, Renshaw, Daniel, Rocklin, Matthew, Romero, Adriana, Roth, Markus, Sadowski, Peter, Salvatier, John, Savard, François, Schlüter, Jan, Schulman, John, Schwartz, Gabriel, Serban, Iulian Vlad, Serdyuk, Dmitriy, Shabanian, Samira, Simon, Étienne, Spieckermann, Sigurd, Subramanyam, S. Ramana, Sygnowski, Jakub, Tanguay, Jérémie, van Tulder, Gijs, Turian, Joseph, Urban, Sebastian, Vincent, Pascal, Visin, Francesco, de Vries, Harm, Warde-Farley, David, Webb, Dustin J., Willson, Matthew, Xu, Kelvin, Xue, Lijun, Yao, Li, Zhang, Saizheng, Zhang, Ying
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially
Externí odkaz:
http://arxiv.org/abs/1605.02688
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, w
Externí odkaz:
http://arxiv.org/abs/1301.3485
Open-text (or open-domain) semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR). Unfortunately, large scale systems cannot be easily machine-learned due to lack of direc
Externí odkaz:
http://arxiv.org/abs/1107.3663
Autor:
Rifai, Salah, Muller, Xavier, Glorot, Xavier, Mesnil, Gregoire, Bengio, Yoshua, Vincent, Pascal
We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other
Externí odkaz:
http://arxiv.org/abs/1104.4153
Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relatively small or heavily contaminated with noise. The regularization of a
Externí odkaz:
http://arxiv.org/abs/1104.3250