Laurent Dinh

According to our database, Laurent Dinh authored at least 22 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures.

Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Generative Modeling with Phase Stochastic Bridge.

Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Joshua M. Susskind, Shuangfei Zhai

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Adaptivity and Modularity for Efficient Generalization Over Task Complexity.

Samira Abnar, Omid Saremi, Laurent Dinh, Shantel Wilson, Miguel Ángel Bautista, Chen Huang, Vimal Thilak, Etai Littwin, Jiatao Gu, Josh M. Susskind, Samy Bengio

CoRR, 2023

Generative Modeling with Phase Stochastic Bridges.

Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Josh M. Susskind, Shuangfei Zhai

CoRR, 2023

2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Perfect Density Models Cannot Guarantee Anomaly Detection.

Charline Le Lan, Laurent Dinh

Entropy, 2021

2020

Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models.

Chin-Wei Huang, Laurent Dinh, Aaron C. Courville

CoRR, 2020

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation.

Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

VideoFlow: A Flow-Based Generative Model for Video.

Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

CoRR, 2019

Discrete Flows: Invertible Generative Models of Discrete Data.

Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Invertible Convolutional Flow.

Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A RAD approach to deep mixture models.

Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2018

Learning Awareness Models.

Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learnable Explicit Density for Continuous Latent Space and Variational Inference.

Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron C. Courville

CoRR, 2017

Sharp Minima Can Generalize For Deep Nets.

Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio

Proceedings of the 34th International Conference on Machine Learning, 2017

Density estimation using Real NVP.

Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Theano: A Python framework for fast computation of mathematical expressions.

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

CoRR, 2016

2015

Techniques for Learning Binary Stochastic Feedforward Neural Networks.

Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh

Proceedings of the 3rd International Conference on Learning Representations, 2015

NICE: Non-linear Independent Components Estimation.

Laurent Dinh, David Krueger, Yoshua Bengio

Proceedings of the 3rd International Conference on Learning Representations, 2015

A Recurrent Latent Variable Model for Sequential Data.

Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Deep independence network analysis of structural brain imaging: A simulation study.

Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun

Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

2013

Predicting Parameters in Deep Learning.

Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013