16 citations · 71 across the 14 of their papers we have counts for
14 papers
CSCO: Connectivity Search of Convolutional Operators
Tunhou Zhang, Shiyu Li, Hsin-Pai Cheng +3
Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations…
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks
Sweta Priyadarshi, Tianyu Jiang, Hsin-Pai Cheng +3
With the growing demand for vision applications and deployment across edge devices, the development of hardware-friendly architectures that maintain performance during device deplo…
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks
Kartikeya Bhardwaj, Hsin-Pai Cheng, Sweta Priyadarshi +1
Zero-Shot Neural Architecture Search (NAS) approaches propose novel training-free metrics called zero-shot proxies to substantially reduce the search time compared to the tradition…
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Hsin-Pai Cheng, Feng Liang, Meng Li +5
Scale variance among different sizes of body parts and objects is a challenging problem for visual recognition tasks. Existing works usually design dedicated backbone or apply Neur…
NASGEM: Neural Architecture Search via Graph Embedding Method
Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang +7
Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architecture…
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin +3
Recent advances demonstrate that irregularly wired neural networks from Neural Architecture Search (NAS) and Random Wiring can not only automate the design of deep neural networks…
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li +4
Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limit…
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang +5
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost. Traditionally, researchers manually craft deep neu…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu +2
In recent years, deep neural networks demonstrated state-of-the-art performance in a large variety of tasks and therefore have been adopted in many applications. On the other hand,…
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Hsin-Pai Cheng, Patrick Yu, Haojing Hu +4
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed…
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo +4
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted…
2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton +38
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technolog…
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Chang Song, Hsin-Pai Cheng, Huanrui Yang +5
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this w…