380 citations · 866 across the 13 of their papers we have counts for
13 papers
UPSCALE: Unconstrained Channel Pruning
Alvin Wan, Hanxiang Hao, Kaushik Patnaik +5
As neural networks grow in size and complexity, inference speeds decline. To combat this, one of the most effective compression techniques -- channel pruning -- removes channels fr…
AutoFocusFormer: Image Segmentation off the Grid
Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai +5
Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small object…
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…
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…
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…
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…
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…
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…
NBDT: Neural-Backed Decision Trees
Alvin Wan, Lisa Dunlap, Daniel Ho +6
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work comb…
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
Vladimir Feinberg, Alvin Wan, Ion Stoica +3
Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complex…
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan, Xiangyu Yue +6
Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadra…
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
Bichen Wu, Alvin Wan, Xiangyu Yue +1
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, ped…
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu, Alvin Wan, Forrest Iandola +2
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time…