Portrait de Derek Nowrouzezahrai

Derek Nowrouzezahrai

Membre académique principal
derek@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Modèles génératifs
Photographie computationnelle
Systèmes dynamiques
Vision par ordinateur
Google Scholar

Biographie

Derek Nowrouzezahrai est professeur titulaire à l'Université McGill, directeur du Centre sur les machines intelligentes et codirecteur du Laboratoire de graphisme et d’imagerie de McGill (MGIL), ainsi que titulaire d’une chaire en IA Canada-CIFAR et de la chaire Ubisoft-Mila de mise à l'échelle des univers de jeux grâce à une IA responsable.

Ses recherches portent sur la simulation de divers phénomènes physiques - tels que la dynamique des objets en mouvement et l'éclairage pour la synthèse d'images réalistes - avec des applications dans les domaines de la réalité virtuelle, des jeux vidéo, de la simulation fluide et contrôlée, de la fabrication numérique, de l'optique augmentée par le calcul et du traitement de la géométrie. En outre, Derek s'intéresse au développement de simulateurs dérivables de ces systèmes dynamiques et à leurs applications aux problèmes inverses en robotique et dans le domaine de la vision.

Son travail repose sur le développement de méthodes Monte Carlo à haute performance et efficaces en matière d'échantillonnage (chaîne de Markov), de statistiques d'ordre élevé et de méthodes de calcul pour les problèmes d'intégration multidimensionnelle complexes, de simulateurs dérivables basés sur la physique et de méthodes numériques pour les systèmes dynamiques, ainsi que sur l'application de l'apprentissage automatique aux médias 3D, visuels et interactifs.

Étudiants actuels

Daniel Bairamian
Doctorat - McGill
Paul Barde
Doctorat - McGill
Co-superviseur⋅e :
Chris Pal
paul.b.barde@gmail.com
Site web
Github
Google Scholar
Chris Emezue
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Chris Pal
Site web
Github
Google Scholar
Florian Golemo
Postdoctorat - McGill
Co-superviseur⋅e :
Gauthier Gidel
Site web
Github
Google Scholar
Daniel Kong
Doctorat - McGill
Github
Ken Ming Lee
Maîtrise recherche - McGill
Site web
Github
Joey Litalien
Doctorat - McGill
Site web
Github
Google Scholar
Hossam Mohamed S M Ahmed
Doctorat - McGill
Github
Somjit Nath
Doctorat - McGill
Superviseur⋅e principal⋅e :
Samira Ebrahimi Kahou
Site web
Google Scholar
Tooba Rahimnia
Maîtrise recherche - McGill
Yasaman Sabbagh Ziarani
Doctorat - McGill
Jithendaraa Subramanian
Maîtrise recherche - McGill
Co-superviseur⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
Yuanyuan Tao
Doctorat - McGill
Mattie Tesfaldet
Doctorat - McGill
Co-superviseur⋅e :
Chris Pal
Site web
Github
Google Scholar

Publications

MeshDiffusion: Score-based Generative 3D Mesh Modeling
Zhen Liu
Yao Feng
Michael J. Black
Derek Nowrouzezahrai
Liam Paull
Weiyang Liu
We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and… (voir plus) physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are more desirable in practice, because (1) they enable easy and arbitrary manipulation of shapes for relighting and simulation, and (2) they can fully leverage the power of modern graphics pipelines which are mostly optimized for meshes. Previous scalable methods for generating meshes typically rely on sub-optimal post-processing, and they tend to produce overly-smooth or noisy surfaces without fine-grained geometric details. To overcome these shortcomings, we take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parameterization. We demonstrate the effectiveness of our model on multiple generative tasks.
2023-02-01
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
From Words to Blocks: Building Objects by Grounding Language Models with Reinforcement Learning
Michael Ahn
Anthony Brohan
Noah Brown
liang Dai
Dan Su
Holy Lovenia Ziwei Bryan Wilie
Tiezheng Yu
Willy Chung
Quyet V. Do
Paul Barde
Tristan Karch
Derek Nowrouzezahrai
C. Bonial
Mitchell Abrams
David R. Traum
Hyung Won
Le Hou
Shayne Longpre
Yi Zoph
William Tay … (voir 32 de plus)
Eric Fedus
Xuezhi Li
Lasse Espeholt
Hubert Soyer
Remi Munos
Karen Si-801
Vlad Mnih
Tom Ward
Yotam Doron
Wenlong Huang
Pieter Abbeel
Deepak Pathak
Julia Kiseleva
Ziming Li
Mohammad Aliannejadi
Shrestha Mohanty
Maartje Ter Hoeve
Mikhail Burtsev
Alexey Skrynnik
Artem Zholus
A. Panov
Kavya Srinet
A. Szlam
Yuxuan Sun
Katja Hofmann
Marc-Alexandre Côté
Ahmed Hamid Awadallah
Linar Abdrazakov
Igor Churin
Putra Manggala
Kata Naszádi
Michiel Van Der Meer
Leveraging pre-trained language models to gen-001 erate action plans for embodied agents is an 002 emerging research direction. However, exe… (voir plus)-003 cuting instructions in real or simulated envi-004 ronments necessitates verifying the feasibility 005 of actions and their relevance in achieving a 006 goal. We introduce a novel method that in-007 tegrates a language model and reinforcement 008 learning for constructing objects in a Minecraft-009 like environment, based on natural language 010 instructions. Our method generates a set of 011 consistently achievable sub-goals derived from 012 the instructions and subsequently completes the 013 associated sub-tasks using a pre-trained RL pol-014 icy. We employ the IGLU competition, which 015 is based on the Minecraft-like simulator, as our 016 test environment, and compare our approach 017 to the competition’s top-performing solutions. 018 Our approach outperforms existing solutions in 019 terms of both the quality of the language model 020 and the quality of the structures built within the 021 IGLU environment. 022
2023-01-01
(publié)
www.semanticscholar.org
Learning Latent Structural Causal Models
Jithendaraa Subramanian
Yashas Annadani
Ivaxi Sheth
Nan Rosemary Ke
Tristan Deleu
Stefan Bauer
Derek Nowrouzezahrai
Samira Ebrahimi Kahou
Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better e… (voir plus)xplanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.
2022-10-24
ArXiv (prépublication)
doi.org
openreview.net
Single‐pass stratified importance resampling
Ege Ciklabakkal
Adrien Gruson
Iliyan Georgiev
Derek Nowrouzezahrai
Toshiya Hachisuka
Resampling is the process of selecting from a set of candidate samples to achieve a distribution (approximately) proportional to a desired t… (voir plus)arget. Recent work has revisited its application to Monte Carlo integration, yielding powerful and practical importance sampling methods. One drawback of existing resampling methods is that they cannot generate stratified samples. We propose two complementary techniques to achieve efficient stratified resampling. We first introduce bidirectional CDF sampling which yields the same result as conventional inverse CDF sampling but in a single pass over the candidates, without needing to store them, similarly to reservoir sampling. We then order the candidates along a space‐filling curve to ensure that stratified CDF sampling of candidate indices yields stratified samples in the integration domain. We showcase our method on various resampling‐based rendering problems.
2022-07-30
Computer Graphics Forum (publié)
doi.org
Kubric: A scalable dataset generator
Klaus Greff
Francois Belletti
Lucas Beyer
Carl Doersch
Yilun Du
Daniel Duckworth
David J Fleet
Dan Gnanapragasam
Florian Golemo
Charles Herrmann
Thomas Kipf
Abhijit Kundu
Dmitry Lagun
Issam Hadj Laradji
Hsueh-Ti Liu
Henning Meyer
Yishu Miao
Derek Nowrouzezahrai
Cengiz Oztireli
Etienne Pot … (voir 14 de plus)
Noha Radwan
Daniel Rebain
Sara Sabour
Mehdi S. M. Sajjadi
Matan Sela
Vincent Sitzmann
Austin Stone
Deqing Sun
Suhani Vora
Ziyu Wang
Tianhao Wu
Kwang Moo Yi
Fangcheng Zhong
Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance o… (voir plus)f a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.
2022-06-18
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org
Kubric: A scalable dataset generator
Klaus Greff
Francois Belletti
Lucas Beyer
Carl Doersch
Yilun Du
Daniel Duckworth
David J. Fleet
Dan Gnanapragasam
Florian Golemo
Charles Herrmann
Thomas N. Kipf
Abhijit Kundu
Dmitry Lagun
Issam Hadj Laradji
Hsueh-Ti Liu
H. Meyer
Yishu Miao
Derek Nowrouzezahrai
Cengiz Oztireli
Etienne Pot … (voir 14 de plus)
Noha Radwan
Daniel Rebain
Sara Sabour
Mehdi S. M. Sajjadi
Matan Sela
Vincent Sitzmann
Austin Stone
Deqing Sun
Suhani Vora
Ziyu Wang
Tianhao Wu
Kwang Moo Yi
Fangcheng Zhong
Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance o… (voir plus)f a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.
2022-03-07
ArXiv (preprint)
doi.org
arxiv.org