activity
20172021
most citedSqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

57 citations · 142 across the 6 of their papers we have counts for

collaborators

10 papers

cs.LG20215 cited

CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks

Robert Avram, Jeffrey E. Olgin, Alvin Wan +8

Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for dia…

cs.CV20204 cited

SegNBDT: Visual Decision Rules for Segmentation

Alvin Wan, Daniel Ho, Younjin Song +3

The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks li…

cs.CV2020

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Bichen Wu, Chenfeng Xu, Xiaoliang Dai +7

Computer vision has achieved remarkable success by (a) representing images as uniformly-arranged pixel arrays and (b) convolving highly-localized features. However, convolutions tr…

cs.CV2020

FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining

Xiaoliang Dai, Alvin Wan, Peizhao Zhang +8

Neural Architecture Search (NAS) yields state-of-the-art neural networks that outperform their best manually-designed counterparts. However, previous NAS methods search for archite…

cs.CR20206 cited

CoVista: A Unified View on Privacy Sensitive Mobile Contact Tracing Effort

David Culler, Prabal Dutta, Gabe Fierro +6

Governments around the world have become increasingly frustrated with tech giants dictating public health policy. The software created by Apple and Google enables individuals to tr…

cs.CV202029 cited

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Alvin Wan, Xiaoliang Dai, Peizhao Zhang +9

Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS's search space i…