Dinh, Laurent

18 publications

NeurIPS 2025 STARFlow: Scaling Latent Normalizing Flows for High-Resolution Image Synthesis Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang Zhang, Laurent Dinh, Miguel Ángel Bautista, Joshua M. Susskind, Shuangfei Zhai

ICLR 2024 Generative Modeling with Phase Stochastic Bridge Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos Theodorou, Joshua M. Susskind, Shuangfei Zhai

ICLR 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

NeurIPS 2022 GAUDI: A Neural Architect for Immersive 3D Scene Generation Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Joshua Susskind

NeurIPSW 2020 Perfect Density Models Cannot Guarantee Anomaly Detection Charline Le Lan, Laurent Dinh

ICLRW 2020 Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models Chin-Wei Huang, Laurent Dinh, Aaron Courville

ICLR 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

ICLRW 2019 A RAD Approach to Deep Mixture Models Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle

NeurIPS 2019 Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

ICLRW 2019 Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

NeurIPS 2019 Invertible Convolutional Flow Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth

ICLR 2018 Learning Awareness Models Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

ICLR 2017 Density Estimation Using Real NVP Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio

ICML 2017 Sharp Minima Can Generalize for Deep Nets Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio

NeurIPS 2015 A Recurrent Latent Variable Model for Sequential Data Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio

ICLR 2015 NICE: Non-Linear Independent Components Estimation Laurent Dinh, David Krueger, Yoshua Bengio

ICLR 2015 Techniques for Learning Binary Stochastic Feedforward Neural Networks Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh

NeurIPS 2013 Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas