Laurent Dinh

36 papers A* 7Journal 20Unranked 9
YearRankTypeTitle / Venue / Authors
2026 J jnl
The Coupling Within: Flow Matching via Distilled Normalizing Flows.
CoRR
David Berthelot, Tianrong Chen, Jiatao Gu, Marco Cuturi, Laurent Dinh, Bhavik Chandna, Michal Klein, Joshua M. Susskind, Shuangfei Zhai
2025 J jnl
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows.
CoRR
Jiatao Gu, Ying Shen, Tianrong Chen, Laurent Dinh, Yuyang Wang, Miguel Ángel Bautista, David Berthelot, Joshua M. Susskind, Shuangfei Zhai
2025 J jnl
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis.
CoRR
Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang Zhang, Laurent Dinh, Miguel Ángel Bautista, Josh M. Susskind, Shuangfei Zhai
2024 A* conf
Generative Modeling with Phase Stochastic Bridge.
ICLR
Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Joshua M. Susskind, Shuangfei Zhai
2024 A* conf
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures.
ICLR
Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin
2023 J jnl
Adaptivity and Modularity for Efficient Generalization Over Task Complexity.
CoRR
Samira Abnar, Omid Saremi, Laurent Dinh, Shantel Wilson, Miguel Ángel Bautista, Chen Huang, Vimal Thilak, Etai Littwin, Jiatao Gu, Josh M. Susskind, Samy Bengio
2023 J jnl
Generative Modeling with Phase Stochastic Bridges.
CoRR
Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Josh M. Susskind, Shuangfei Zhai
2023 J jnl
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures.
CoRR
Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin
2022 A* conf
GAUDI: A Neural Architect for Immersive 3D Scene Generation.
NeurIPS
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
2022 J jnl
GAUDI: A Neural Architect for Immersive 3D Scene Generation.
CoRR
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
2021 J jnl
Perfect Density Models Cannot Guarantee Anomaly Detection.
Entropy
Charline Le Lan, Laurent Dinh
2020 J jnl
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models.
CoRR
Chin-Wei Huang, Laurent Dinh, Aaron C. Courville
2020 J jnl
Perfect density models cannot guarantee anomaly detection.
CoRR
Charline Le Lan, Laurent Dinh
2020 A* conf
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation.
ICLR
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma
2019 — conf
A RAD approach to deep mixture models.
DGS@ICLR
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle
2019 J jnl
A RAD approach to deep mixture models.
CoRR
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle
2019 — conf
Discrete Flows: Invertible Generative Models of Discrete Data.
DGS@ICLR
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019 A* conf
Discrete Flows: Invertible Generative Models of Discrete Data.
NeurIPS
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019 J jnl
Discrete Flows: Invertible Generative Models of Discrete Data.
CoRR
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
2019 A* conf
Invertible Convolutional Flow.
NeurIPS
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth
2019 J jnl
VideoFlow: A Flow-Based Generative Model for Video.
CoRR
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma
2018 — conf
Learning Awareness Models.
ICLR (Poster)
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil
2018 J jnl
Learning Awareness Models.
CoRR
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil
2017 — conf
Density estimation using Real NVP.
ICLR (Poster)
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
2017 J jnl
Learnable Explicit Density for Continuous Latent Space and Variational Inference.
CoRR
Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron C. Courville
2017 A* conf
Sharp Minima Can Generalize For Deep Nets.
ICML
Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio
2017 J jnl
Sharp Minima Can Generalize For Deep Nets.
CoRR
Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio
2016 J jnl
Density estimation using Real NVP.
CoRR
Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
2016 J jnl
Theano: A Python framework for fast computation of mathematical expressions.
CoRR
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
2015 — conf
A Recurrent Latent Variable Model for Sequential Data.
NIPS
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio
2015 J jnl
A Recurrent Latent Variable Model for Sequential Data.
CoRR
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio
2015 — conf
Deep independence network analysis of structural brain imaging: A simulation study.
MLSP
Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun
2015 — conf
NICE: Non-linear Independent Components Estimation.
ICLR (Workshop)
Laurent Dinh, David Krueger, Yoshua Bengio
2015 — conf
Techniques for Learning Binary Stochastic Feedforward Neural Networks.
ICLR (Poster)
Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh
2013 — conf
Predicting Parameters in Deep Learning.
NIPS
Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
2013 J jnl
Predicting Parameters in Deep Learning.
CoRR
Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas