Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Hidasi, Balázs"'
Coupling latent diffusion based image generation with contextual bandits enables the creation of eye-catching personalized product images at scale that was previously either impossible or too expensive. In this paper we showcase how we utilized these
Externí odkaz:
http://arxiv.org/abs/2408.12392
Autor:
Hidasi, Balázs, Czapp, Ádám Tibor
Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the research community has also started discussing various aspects of evaluation
Externí odkaz:
http://arxiv.org/abs/2307.14956
Autor:
Hidasi, Balázs, Czapp, Ádám Tibor
Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, sinc
Externí odkaz:
http://arxiv.org/abs/2307.14951
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. Whil
Externí odkaz:
http://arxiv.org/abs/1706.04148
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recomme
Externí odkaz:
http://arxiv.org/abs/1706.03847
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
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of
Externí odkaz:
http://arxiv.org/abs/1511.06939
Autor:
Hidasi, Balázs, Tikk, Domonkos
Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to
Externí odkaz:
http://arxiv.org/abs/1401.4529
Autor:
Hidasi, Balázs, Tikk, Domonkos
Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-o
Externí odkaz:
http://arxiv.org/abs/1309.7611
Autor:
Hidasi, Balázs, Tikk, Domonkos
Publikováno v:
Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-
Externí odkaz:
http://arxiv.org/abs/1204.1259