5 citations · 14 across the 4 of their papers we have counts for
6 papers
CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance
Tianchen Zhao, Niansong Zhang, Xuefei Ning +3
Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on…
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
Xuefei Ning, Junbo Zhao, Wenshuo Li +4
Convolutional neural networks (CNNs) are vulnerable to adversarial examples, and studies show that increasing the model capacity of an architecture topology (e.g., width expansion)…
aw_nas: A Modularized and Extensible NAS framework
Xuefei Ning, Changcheng Tang, Wenshuo Li +7
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner. aw_nas is an open-source Py…
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures
Tianchen Zhao, Xuefei Ning, Xiangsheng Shi +6
Binary Neural Networks (BNNs) have received significant attention due to their promising efficiency. Currently, most BNN studies directly adopt widely-used CNN architectures, which…
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
Xuefei Ning, Tianchen Zhao, Wenshuo Li +3
Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key pro…
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Xuefei Ning, Yin Zheng, Tianchen Zhao +2
This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from ex…