Zobrazeno 1 - 10
of 13
pro vyhledávání: '"de Brébisson, Alexandre"'
Autor:
Kumar, Kundan, Kumar, Rithesh, de Boissiere, Thibault, Gestin, Lucas, Teoh, Wei Zhen, Sotelo, Jose, de Brebisson, Alexandre, Bengio, Yoshua, Courville, Aaron
Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveform
Externí odkaz:
http://arxiv.org/abs/1910.06711
Autor:
Serban, Iulian V., Sankar, Chinnadhurai, Germain, Mathieu, Zhang, Saizheng, Lin, Zhouhan, Subramanian, Sandeep, Kim, Taesup, Pieper, Michael, Chandar, Sarath, Ke, Nan Rosemary, Rajeswar, Sai, de Brebisson, Alexandre, Sotelo, Jose M. R., Suhubdy, Dendi, Michalski, Vincent, Nguyen, Alexandre, Pineau, Joelle, Bengio, Yoshua
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through bot
Externí odkaz:
http://arxiv.org/abs/1801.06700
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and doe
Externí odkaz:
http://arxiv.org/abs/1801.01442
Autor:
Serban, Iulian V., Sankar, Chinnadhurai, Germain, Mathieu, Zhang, Saizheng, Lin, Zhouhan, Subramanian, Sandeep, Kim, Taesup, Pieper, Michael, Chandar, Sarath, Ke, Nan Rosemary, Rajeshwar, Sai, de Brebisson, Alexandre, Sotelo, Jose M. R., Suhubdy, Dendi, Michalski, Vincent, Nguyen, Alexandre, Pineau, Joelle, Bengio, Yoshua
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through bot
Externí odkaz:
http://arxiv.org/abs/1709.02349
The softmax content-based attention mechanism has proven to be very beneficial in many applications of recurrent neural networks. Nevertheless it suffers from two major computational limitations. First, its computations for an attention lookup scale
Externí odkaz:
http://arxiv.org/abs/1609.05866
An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the prob
Externí odkaz:
http://arxiv.org/abs/1606.08061
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
Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of proble
Externí odkaz:
http://arxiv.org/abs/1604.08859
In a multi-class classification problem, it is standard to model the output of a neural network as a categorical distribution conditioned on the inputs. The output must therefore be positive and sum to one, which is traditionally enforced by a softma
Externí odkaz:
http://arxiv.org/abs/1511.05042
We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence o
Externí odkaz:
http://arxiv.org/abs/1508.00021