activity
20162022
most citedPolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

85 citations · 141 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2022★ 17 cited

PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds

Zhaoqi Leng, Shuyang Cheng, Benjamin Caine +5

Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been…

cs.LG2022

G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR

Gary Wang, Ekin D. Cubuk, Andrew Rosenberg +6

Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has beco…

cs.CV2022

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

Chenxi Liu, Zhaoqi Leng, Pei Sun +5

Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-…

cs.CV2022★ 85 cited

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Zhaoqi Leng, Mingxing Tan, Chenxi Liu +4

Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can…

cs.CV2021

Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset

Scott Ettinger, Shuyang Cheng, Benjamin Caine +15

As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive situatio…

cs.LG2020★ 11 cited

Can weight sharing outperform random architecture search? An investigation with TuNAS

Gabriel Bender, Hanxiao Liu, Bo Chen +4

Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models. There is, howev…

cs.CV2020★ 12 cited

Improving 3D Object Detection through Progressive Population Based Augmentation

Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk +10

Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentati…

math.AG2016★ 16 cited

On a conjecture of Braverman and Kazhdan

S. Cheng, B. C. Ngo

In this paper a proof of Conjecture 9.12 of Braverman and Kazhdan in their article "gamma-functions of representations and lifting" on the acyclicity of their l-adic gamma-sheaves…