29 citations · 42 across the 3 of their papers we have counts for
7 papers
Fast Point Cloud Generation with Straight Flows
Lemeng Wu, Dilin Wang, Chengyue Gong +6
Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise i…
SageMix: Saliency-Guided Mixup for Point Clouds
Sanghyeok Lee, Minkyu Jeon, Injae Kim +2
Data augmentation is key to improving the generalization ability of deep learning models. Mixup is a simple and widely-used data augmentation technique that has proven effective in…
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty +4
Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the s…
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Yunyang Xiong, Hanxiao Liu, Suyog Gupta +7
Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In…
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?
Yunyang Xiong, Ronak Mehta, Vikas Singh
The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning…
ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification
Yunyang Xiong, Hyunwoo J. Kim, Varsha Hedau
Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance.…