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ORCIDLaurent Dinh
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2020 – today
- 2024
[c16]Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Joshua M. Susskind, Shuangfei Zhai:
Generative Modeling with Phase Stochastic Bridge. ICLR 2024- [c15]Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. ICLR 2024 - 2023
- [i16]Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Josh M. Susskind, Shuangfei Zhai:
Generative Modeling with Phase Stochastic Bridges. CoRR abs/2310.07805 (2023) - [i15]Samira Abnar, Omid Saremi, Laurent Dinh, Shantel Wilson, Miguel Ángel Bautista, Chen Huang, Vimal Thilak, Etai Littwin, Jiatao Gu, Josh M. Susskind, Samy Bengio:
Adaptivity and Modularity for Efficient Generalization Over Task Complexity. CoRR abs/2310.08866 (2023) - [i14]Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin:
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures. CoRR abs/2312.04000 (2023) - 2022
- [c14]Miguel Ángel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Joshua M. Susskind:
GAUDI: A Neural Architect for Immersive 3D Scene Generation. NeurIPS 2022 - [i13]Miguel Ángel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Josh M. Susskind:
GAUDI: A Neural Architect for Immersive 3D Scene Generation. CoRR abs/2207.13751 (2022) - 2021
- [j1]Charline Le Lan, Laurent Dinh:
Perfect Density Models Cannot Guarantee Anomaly Detection. Entropy 23(12): 1690 (2021) - 2020
- [c13]Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma:
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. ICLR 2020 - [i12]Chin-Wei Huang, Laurent Dinh, Aaron C. Courville:
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models. CoRR abs/2002.07101 (2020) - [i11]Charline Le Lan, Laurent Dinh:
Perfect density models cannot guarantee anomaly detection. CoRR abs/2012.03808 (2020)
2010 – 2019
- 2019
- [c12]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. DGS@ICLR 2019 - [c11]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. DGS@ICLR 2019 - [c10]Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth:
Invertible Convolutional Flow. NeurIPS 2019: 5636-5646 - [c9]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. NeurIPS 2019: 14692-14701 - [i10]Manoj Kumar
, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma:
VideoFlow: A Flow-Based Generative Model for Video. CoRR abs/1903.01434 (2019) - [i9]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. CoRR abs/1903.07714 (2019) - [i8]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. CoRR abs/1905.10347 (2019) - 2018
- [c8]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. ICLR (Poster) 2018 - [i7]Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil:
Learning Awareness Models. CoRR abs/1804.06318 (2018) - 2017
- [c7]Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio:
Density estimation using Real NVP. ICLR (Poster) 2017 - [c6]Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio:
Sharp Minima Can Generalize For Deep Nets. ICML 2017: 1019-1028 - [i6]Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio:
Sharp Minima Can Generalize For Deep Nets. CoRR abs/1703.04933 (2017) - [i5]Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron C. Courville:
Learnable Explicit Density for Continuous Latent Space and Variational Inference. CoRR abs/1710.02248 (2017) - 2016
- [i4]Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermüller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph P. Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016) - [i3]Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio:
Density estimation using Real NVP. CoRR abs/1605.08803 (2016) - 2015
- [c5]Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun:
Deep independence network analysis of structural brain imaging: A simulation study. MLSP 2015: 1-6 - [c4]Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio:
A Recurrent Latent Variable Model for Sequential Data. NIPS 2015: 2980-2988 - [c3]Laurent Dinh, David Krueger, Yoshua Bengio:
NICE: Non-linear Independent Components Estimation. ICLR (Workshop) 2015 - [c2]Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh:
Techniques for Learning Binary Stochastic Feedforward Neural Networks. ICLR (Poster) 2015 - [i2]Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio:
A Recurrent Latent Variable Model for Sequential Data. CoRR abs/1506.02216 (2015) - 2013
- [c1]Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas:
Predicting Parameters in Deep Learning. NIPS 2013: 2148-2156 - [i1]Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas:
Predicting Parameters in Deep Learning. CoRR abs/1306.0543 (2013)
Coauthor Index
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