Search Results for author: Chun Jason Xue

Found 7 papers, 4 papers with code

2D-Empowered 3D Object Detection on the Edge

no code implementations18 Feb 2023 Jingzong Li, Yik Hong Cai, Libin Liu, Yu Mao, Chun Jason Xue, Hong Xu

Our main contributions are two-fold: First, we design a 2D-to-3D transformation pipeline that takes as input the point cloud data from LiDAR and 2D bounding boxes from camera that are captured at exactly the same time, and generate 3D bounding boxes efficiently and accurately based on detection results of the previous frames without running 3D detectors.

3D Object Detection Autonomous Driving +1

NFL: Robust Learned Index via Distribution Transformation

1 code implementation24 May 2022 Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, Chun Jason Xue

Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).

A Fast Transformer-based General-Purpose Lossless Compressor

1 code implementation30 Mar 2022 Yu Mao, Yufei Cui, Tei-Wei Kuo, Chun Jason Xue

To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.

Variational Nested Dropout

1 code implementation CVPR 2021 Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue

Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.

Representation Learning

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex

no code implementations25 Sep 2019 Yufei Cui, Wuguannan Yao, Qiao Li, Antoni Chan, Chun Jason Xue

In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.

Adversarial Attack Bayesian Inference +1

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

1 code implementation29 May 2019 Yufei Cui, Wuguannan Yao, Qiao Li, Antoni B. Chan, Chun Jason Xue

In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.

Adversarial Attack Bayesian Inference +2

EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing

no code implementations5 Jun 2018 Jianzhong Sheng, Chuanbo Chen, Chenchen Fu, Chun Jason Xue

Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs).

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