1 code implementation • 17 Jul 2023 • Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad, David Güera, Zhile Ren, Qi Shan
However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies.
1 code implementation • CVPR 2023 • Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alex Schwing, Alex Colburn, Li Fuxin
To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task.
Ranked #4 on Instance Segmentation on Cityscapes val
no code implementations • 14 Jun 2021 • Robert Avram, Jeffrey E. Olgin, Alvin Wan, Zeeshan Ahmed, Louis Verreault-Julien, Sean Abreau, Derek Wan, Joseph E. Gonzalez, Derek Y. So, Krishan Soni, Geoffrey H. Tison
Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms.
no code implementations • ICLR 2021 • Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
no code implementations • ICCV 2021 • Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph E. Gonzalez, Kurt Keutzer, Peter Vajda
A recent trend in computer vision is to replace convolutions with transformers.
1 code implementation • 11 Jun 2020 • Alvin Wan, Daniel Ho, Younjin Song, Henk Tillman, Sarah Adel Bargal, Joseph E. Gonzalez
To address this, prior work combines neural networks with decision trees.
8 code implementations • 5 Jun 2020 • Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, Peter Vajda
In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships.
2 code implementations • CVPR 2021 • Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph E. Gonzalez
To address this, we present Neural Architecture-Recipe Search (NARS) to search both (a) architectures and (b) their corresponding training recipes, simultaneously.
Ranked #5 on Neural Architecture Search on ImageNet
1 code implementation • CVPR 2020 • Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, Peter Vajda, Joseph E. Gonzalez
We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands.
Ranked #68 on Neural Architecture Search on ImageNet
2 code implementations • 1 Apr 2020 • Alvin Wan, Lisa Dunlap, Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah Adel Bargal, Joseph E. Gonzalez
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
no code implementations • 28 Feb 2018 • Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael. I. Jordan, Joseph E. Gonzalez, Sergey Levine
By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.
2 code implementations • CVPR 2018 • Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
Neural networks rely on convolutions to aggregate spatial information.
6 code implementations • 19 Oct 2017 • Bichen Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.
Ranked #22 on Robust 3D Semantic Segmentation on SemanticKITTI-C
13 code implementations • 4 Dec 2016 • Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer
In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.