Search Results for author: Laurent Dinh

Found 20 papers, 10 papers with code

LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

no code implementations7 Dec 2023 Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin

In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures.

Adaptivity and Modularity for Efficient Generalization Over Task Complexity

no code implementations13 Oct 2023 Samira Abnar, Omid Saremi, Laurent Dinh, Shantel Wilson, Miguel Angel Bautista, Chen Huang, Vimal Thilak, Etai Littwin, Jiatao Gu, Josh Susskind, Samy Bengio

We investigate how the use of a mechanism for adaptive and modular computation in transformers facilitates the learning of tasks that demand generalization over the number of sequential computation steps (i. e., the depth of the computation graph).


Generative Modeling with Phase Stochastic Bridges

1 code implementation11 Oct 2023 Tianrong Chen, Jiatao Gu, Laurent Dinh, Evangelos A. Theodorou, Joshua Susskind, Shuangfei Zhai

In this work, we introduce a novel generative modeling framework grounded in \textbf{phase space dynamics}, where a phase space is defined as {an augmented space encompassing both position and velocity.}

Image Generation Position

GAUDI: A Neural Architect for Immersive 3D Scene Generation

1 code implementation27 Jul 2022 Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Josh Susskind

We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.

Image Generation Scene Generation

Perfect density models cannot guarantee anomaly detection

no code implementations7 Dec 2020 Charline Le Lan, Laurent Dinh

Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning.

Active Learning Anomaly Detection +1

Solving ODE with Universal Flows: Approximation Theory for Flow-Based Models

no code implementations ICLR Workshop DeepDiffEq 2019 Chin-wei Huang, Laurent Dinh, Aaron Courville

Normalizing flows are powerful invertible probabilistic models that can be used to translate two probability distributions, in a way that allows us to efficiently track the change of probability density.

Computational Efficiency

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

1 code implementation17 Feb 2020 Chin-wei Huang, Laurent Dinh, Aaron Courville

In this work, we propose a new family of generative flows on an augmented data space, with an aim to improve expressivity without drastically increasing the computational cost of sampling and evaluation of a lower bound on the likelihood.

Image Generation

Invertible Convolutional Flow

1 code implementation NeurIPS 2019 Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth

We show that these transforms allow more effective normalizing flow models to be developed for generative image models.

Discrete Flows: Invertible Generative Models of Discrete Data

2 code implementations NeurIPS 2019 Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole

While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.

Language Modelling

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

1 code implementation ICLR 2020 Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.

Predict Future Video Frames Video Generation

Learning Awareness Models

no code implementations ICLR 2018 Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.

Learnable Explicit Density for Continuous Latent Space and Variational Inference

no code implementations6 Oct 2017 Chin-wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron Courville

In this paper, we study two aspects of the variational autoencoder (VAE): the prior distribution over the latent variables and its corresponding posterior.

Density Estimation Variational Inference

Sharp Minima Can Generalize For Deep Nets

no code implementations ICML 2017 Laurent Dinh, Razvan Pascanu, Samy Bengio, Yoshua Bengio

Despite their overwhelming capacity to overfit, deep learning architectures tend to generalize relatively well to unseen data, allowing them to be deployed in practice.

Density estimation using Real NVP

33 code implementations27 May 2016 Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.

Ranked #22 on Image Generation on ImageNet 32x32 (bpd metric)

BIG-bench Machine Learning Density Estimation +1

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, 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 Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, 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 Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher 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 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

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

BIG-bench Machine Learning Clustering +2

A Recurrent Latent Variable Model for Sequential Data

5 code implementations NeurIPS 2015 Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio

In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder.

NICE: Non-linear Independent Components Estimation

19 code implementations30 Oct 2014 Laurent Dinh, David Krueger, Yoshua Bengio

It is based on the idea that a good representation is one in which the data has a distribution that is easy to model.

Ranked #73 on Image Generation on CIFAR-10 (bits/dimension metric)

Image Generation

Techniques for Learning Binary Stochastic Feedforward Neural Networks

no code implementations11 Jun 2014 Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh

Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.

Structured Prediction

Predicting Parameters in Deep Learning

no code implementations NeurIPS 2013 Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas

We demonstrate that there is significant redundancy in the parameterization of several deep learning models.

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