121 citations · 193 across the 16 of their papers we have counts for
16 papers
SphereFed: Hyperspherical Federated Learning
Xin Dong, Sai Qian Zhang, Ang Li +1
Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling o…
DRESS: Dynamic REal-time Sparse Subnets
Zhongnan Qu, Syed Shakib Sarwar, Xin Dong +3
The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource con…
Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration
Yuhang Li, Shikuang Deng, Xin Dong +1
Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks. Conventionally, SNNs can be obtain…
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
Xin Dong, Barbara De Salvo, Meng Li +4
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to…
Fast query-by-example speech search using separable model
Yuguang Yang, Yu Pan, Xin Dong +1
Traditional Query-by-Example (QbE) speech search approaches usually use methods based on frame-level features, while state-of-the-art approaches tend to use models based on acousti…
A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong +2
Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the…
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
Xin Dong, Junfeng Guo, Ang Li +3
Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from exist…
MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing
Yuhang Li, Feng Zhu, Ruihao Gong +5
User data confidentiality protection is becoming a rising challenge in the present deep learning research. Without access to data, conventional data-driven model compression faces…
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Yuhang Li, Wei Wang, Haoli Bai +3
Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activati…
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
Zuohui Fu, Yikun Xian, Shijie Geng +5
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in…
RTN: Reparameterized Ternary Network
Yuhang Li, Xin Dong, Sai Qian Zhang +3
To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed…
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks
Yuhang Li, Xin Dong, Wei Wang
We propose Additive Powers-of-Two~(APoT) quantization, an efficient non-uniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in n…
Full-stack Optimization for Accelerating CNNs with FPGA Validation
Bradley McDanel, Sai Qian Zhang, H. T. Kung +1
We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with field-programmable gate arrays (FPG…
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network
Yinghao Xu, Xin Dong, Yudian Li +1
To reduce memory footprint and run-time latency, techniques such as neural network pruning and binarization have been explored separately. However, it is unclear how to combine the…
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
Shilin Zhu, Xin Dong, Hao Su
Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the effi…
Discovering Multiple Truths with a Hybrid Model
Furong Li, Xin Luna Dong, Anno Langen +1
Many data management applications require integrating information from multiple sources. The sources may not be accurate and provide erroneous values. We thus have to identify the…