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Zhen Liu

Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e
Liam Paull
Co-supervisor
Yoshua Bengio
Sujets de recherche
Apprentissage profond

Billets de blogue

Visuel de l'Article sur la représentation du maillage non étanche de t-shirts
15 mai 2024
Comment représenter efficacement le maillage non étanche de t-shirts?
par
Zhen Liu
Yao Feng
Yuliang Xiu
Weiyang Liu
Liam Paull
Michael J. Black
Bernhard Scholkopf
Lire l'article

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
Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning
Yuchen Lu
Zhen Liu
Aristide Baratin
Romain Laroche
Aaron Courville
Alessandro Sordoni
2023-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu
Zhen Liu
Aristide Baratin
Romain Laroche
Aaron Courville
Alessandro Sordoni
2023-01-01
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang
Nikolay Malkin
Zhen Liu
Alexandra Volokhova
Aaron Courville
Yoshua Bengio
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Buil… (voir plus)ding upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic data construction policy and thus amortize expensive MCMC exploration into a fixed number of actions sampled from a GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs sampling to mix between modes. We propose a framework to jointly train a GFlowNet with an energy function, so that the GFlowNet learns to sample from the energy distribution, while the energy learns with an approximate MLE objective with negative samples from the GFlowNet. We demonstrate EB-GFN's effectiveness on various probabilistic modeling tasks. Code is publicly available at https://github.com/zdhNarsil/EB_GFN.
2022-06-28
Proceedings of the 39th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu
Zhen Liu
Aristide Baratin
Romain Laroche
Aaron Courville
Alessandro Sordoni
2022-06-02
ArXiv (prépublication)
arxiv.org
arxiv.org
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu
Zhen Liu
Aristide Baratin
Romain Laroche
Aaron Courville
Alessandro Sordoni
2022-06-02
ArXiv (prépublication)
arxiv.org
arxiv.org
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Harry Zhao
Mingde Zhao
Zhen Liu
Sitao Luan
Shuyuan Zhang
Doina Precup
Yoshua Bengio
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during plan… (voir plus)ning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.
openreview.net