69 citations · 365 across the 24 of their papers we have counts for
33 papers
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models
Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal +1
Neural architectures can be naturally viewed as computational graphs. Motivated by this perspective, we, in this paper, study neural architecture search (NAS) through the lens of l…
Learning Latent Part-Whole Hierarchies for Point Clouds
Xiang Gao, Wei Hu, Renjie Liao
Strong evidence suggests that humans perceive the 3D world by parsing visual scenes and objects into part-whole hierarchies. Although deep neural networks have the capability of le…
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
Sahithya Ravi, Aditya Chinchure, Leonid Sigal +2
There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus…
Gaussian-Bernoulli RBMs Without Tears
Renjie Liao, Simon Kornblith, Mengye Ren +2
We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann machines (GRBMs), introducing two innovations. We propose a novel Gibbs-Langevin sampling alg…
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
Xiaohui Zeng, Raquel Urtasun, Richard Zemel +2
In this paper, we present a non-parametric structured latent variable model for image generation, called NP-DRAW, which sequentially draws on a latent canvas in a part-by-part fash…
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
Wenyuan Zeng, Ming Liang, Renjie Liao +1
Forecasting the future behaviors of dynamic actors is an important task in many robotics applications such as self-driving. It is extremely challenging as actors have latent intent…