no code implementations • 26 Apr 2024 • Tunhou Zhang, Shiyu Li, Hsin-Pai Cheng, Feng Yan, Hai Li, Yiran Chen
In this paper, we propose CSCO, a novel paradigm that fabricates effective connectivity of convolutional operators with minimal utilization of existing design motifs and further utilizes the discovered wiring to construct high-performing ConvNets.
no code implementations • 13 Feb 2024 • Tunhou Zhang, Feng Yan, Hai Li, Yiran Chen
The utilization of residual learning has become widespread in deep and scalable neural nets.
no code implementations • 1 Nov 2023 • Tunhou Zhang, Wei Wen, Igor Fedorov, Xi Liu, Buyun Zhang, Fangqiu Han, Wen-Yen Chen, Yiping Han, Feng Yan, Hai Li, Yiran Chen
To optimize search efficiency, DistDNAS distributes the search and aggregates the choice of optimal interaction modules on varying data dates, achieving over 25x speed-up and reducing search cost from 2 days to 2 hours.
no code implementations • 31 Oct 2023 • Yufan Cao, Tunhou Zhang, Wei Wen, Feng Yan, Hai Li, Yiran Chen
FGPS enhances path diversity to facilitate more comprehensive supernet exploration, while emphasizing path quality to ensure the effective identification and utilization of promising architectures.
no code implementations • 6 Jul 2023 • Bhavna Gopal, Arjun Sridhar, Tunhou Zhang, Yiran Chen
We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance.
1 code implementation • 28 Nov 2022 • Tunhou Zhang, Mingyuan Ma, Feng Yan, Hai Li, Yiran Chen
In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data.
Ranked #6 on Robust 3D Semantic Segmentation on SemanticKITTI-C
Neural Architecture Search Robust 3D Semantic Segmentation +1
2 code implementations • 14 Jul 2022 • Tunhou Zhang, Dehua Cheng, Yuchen He, Zhengxing Chen, Xiaoliang Dai, Liang Xiong, Feng Yan, Hai Li, Yiran Chen, Wei Wen
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
no code implementations • 30 Mar 2022 • Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, Yiran Chen
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
no code implementations • 29 Sep 2021 • Tunhou Zhang, Mingyuan Ma, Feng Yan, Hai Li, Yiran Chen
MAKPConv employs a depthwise kernel to reduce resource consumption and re-calibrates the contribution of kernel points towards each neighbor point via Neighbor-Kernel attention to improve representation power.
no code implementations • 3 Dec 2020 • Chen-Chia Chang, Jingyu Pan, Tunhou Zhang, Zhiyao Xie, Jiang Hu, Weiyi Qi, Chun-Wei Lin, Rongjian Liang, Joydeep Mitra, Elias Fallon, Yiran Chen
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
no code implementations • 8 Jul 2020 • Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang, Shi-Yu Li, Feng Liang, Feng Yan, Meng Li, Vikas Chandra, Hai Li, Yiran Chen
To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.
1 code implementation • 21 Nov 2019 • Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li, Feng Yan, Chengyu Huang, Hai Li, Yiran Chen
Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1. 5 GPU hours, which is 7. 2x and 6. 7x faster than the crafting time of SOTA CNN and RNN models, respectively.
1 code implementation • 19 Jun 2019 • Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang, Feng Yan, Shi-Yu Li, Harris Teague, Hai Li, Yiran Chen
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.