353 citations
- University of TorontoCA7 papers
- University of CambridgeGB3 papers
- University of Central FloridaUS3 papers
- Amazon (Germany)DE2 papers
- Meta (Israel)IL2 papers
- OpenAI (United States)US2 papers
- University of AmsterdamNL2 papers
- University of WaterlooCA2 papers
- Aalborg UniversityDK1 paper
- AIT Austrian Institute of Technology GmbHAT1 paper
- Ames Research CenterUS1 paper
- Arizona State UniversityUS1 paper
7 papers · 1 filter
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…
Deep Feedback Inverse Problem Solver
Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…
Learning Joint 2D-3D Representations for Depth Completion
Yun Chen, Bin Yang, Ming Liang +1
In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D an…
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Ming Liang, Bin Yang, Yun Chen +2
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that rea…
Learning Lane Graph Representations for Motion Forecasting
Ming Liang, Bin Yang, Rui Hu +4
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, w…
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI
Saifeng Liu, Huaixiu Zheng, Yesu Feng +1
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MR…