16 citations · 39 across the 10 of their papers we have counts for
10 papers
Curious Replay for Model-based Adaptation
Isaac Kauvar, Chris Doyle, Linqi Zhou +1
Agents must be able to adapt quickly as an environment changes. We find that existing model-based reinforcement learning agents are unable to do this well, in part because of how t…
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models
Gimin Nam, Mariem Khlifi, Andrew Rodriguez +3
Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shape…
Deep Latent State Space Models for Time-Series Generation
Linqi Zhou, Michael Poli, Winnie Xu +2
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly comp…
ButterflyFlow: Building Invertible Layers with Butterfly Matrices
Chenlin Meng, Linqi Zhou, Kristy Choi +2
Normalizing flows model complex probability distributions using maps obtained by composing invertible layers. Special linear layers such as masked and 1x1 convolutions play a key r…
Emergence of Theory of Mind Collaboration in Multiagent Systems
Luyao Yuan, Zipeng Fu, Linqi Zhou +2
Currently, in the study of multiagent systems, the intentions of agents are usually ignored. Nonetheless, as pointed out by Theory of Mind (ToM), people regularly reason about othe…
3D Shape Generation and Completion through Point-Voxel Diffusion
Linqi Zhou, Yilun Du, Jiajun Wu
We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, ou…
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
Tian Han, Erik Nijkamp, Linqi Zhou +3
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are b…
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Erik Nijkamp, Bo Pang, Tian Han +3
This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models h…
Deep Unsupervised Clustering with Clustered Generator Model
Dandan Zhu, Tian Han, Linqi Zhou +2
This paper addresses the problem of unsupervised clustering which remains one of the most fundamental challenges in machine learning and artificial intelligence. We propose the clu…
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
Zijun Zhang, Linqi Zhou, Liangke Gou +1
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep…