Search Results for author: Peisong Wang

Found 22 papers, 9 papers with code

ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices

no code implementations ECCV 2020 Xiangyu He, Zitao Mo, Ke Cheng, Weixiang Xu, Qinghao Hu, Peisong Wang, Qingshan Liu, Jian Cheng

The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination.

Binarization Quantization

Towards Accurate Post-training Network Quantization via Bit-Split and Stitching

1 code implementation ICML 2020 Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng

Network quantization is essential for deploying deep models to IoT devices due to the high efficiency, no matter on special hardware like TPU or general hardware like CPU and GPU.

Image Classification Instance Segmentation +4

ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs

no code implementations17 Feb 2024 Yuhan Li, Peisong Wang, ZHIXUN LI, Jeffrey Xu Yu, Jia Li

The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.

Graph Learning Language Modelling +2

A Survey of Graph Meets Large Language Model: Progress and Future Directions

1 code implementation21 Nov 2023 Yuhan Li, ZHIXUN LI, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, Jeffrey Xu Yu

First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.

Language Modelling Large Language Model

Soft Threshold Ternary Networks

1 code implementation4 Apr 2022 Weixiang Xu, Xiangyu He, Tianli Zhao, Qinghao Hu, Peisong Wang, Jian Cheng

The latest STTN shows that ResNet-18 with ternary weights and ternary activations achieves up to 68. 2% Top-1 accuracy on ImageNet.


Differentially Private Federated Learning with Local Regularization and Sparsification

no code implementations CVPR 2022 Anda Cheng, Peisong Wang, Xi Sheryl Zhang, Jian Cheng

User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning.

Federated Learning

Q-ViT: Fully Differentiable Quantization for Vision Transformer

no code implementations19 Jan 2022 Zhexin Li, Tong Yang, Peisong Wang, Jian Cheng

In this paper, we propose a fully differentiable quantization method for vision transformer (ViT) named as Q-ViT, in which both of the quantization scales and bit-widths are learnable parameters.


DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy

1 code implementation16 Oct 2021 Anda Cheng, Jiaxing Wang, Xi Sheryl Zhang, Qiang Chen, Peisong Wang, Jian Cheng

In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS.

Neural Architecture Search

Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization

no code implementations ICCV 2021 Weihan Chen, Peisong Wang, Jian Cheng

Finally, based on the above simplification, we show that the original problem can be reformulated as a Multiple-Choice Knapsack Problem (MCKP) and propose a greedy search algorithm to solve it efficiently.

Multiple-choice Quantization

Improving Binary Neural Networks through Fully Utilizing Latent Weights

no code implementations12 Oct 2021 Weixiang Xu, Qiang Chen, Xiangyu He, Peisong Wang, Jian Cheng

Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W to help binary training.

Generative Zero-shot Network Quantization

no code implementations21 Jan 2021 Xiangyu He, Qinghao Hu, Peisong Wang, Jian Cheng

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration.

Data Free Quantization Image Generation

Location-aware Upsampling for Semantic Segmentation

1 code implementation13 Nov 2019 Xiangyu He, Zitao Mo, Qiang Chen, Anda Cheng, Peisong Wang, Jian Cheng

Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks.

Decoder Segmentation +1

SpatialFlow: Bridging All Tasks for Panoptic Segmentation

1 code implementation19 Oct 2019 Qiang Chen, Anda Cheng, Xiangyu He, Peisong Wang, Jian Cheng

Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene.

Instance Segmentation Object +3

A System-Level Solution for Low-Power Object Detection

no code implementations24 Sep 2019 Fanrong Li, Zitao Mo, Peisong Wang, Zejian Liu, Jiayun Zhang, Gang Li, Qinghao Hu, Xiangyu He, Cong Leng, Yang Zhang, Jian Cheng

As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6. 9W (with display) with an mAP of 66. 4 verified on the PASCAL VOC 2012 dataset.

Object object-detection +2

Compact Global Descriptor for Neural Networks

1 code implementation23 Jul 2019 Xiangyu He, Ke Cheng, Qiang Chen, Qinghao Hu, Peisong Wang, Jian Cheng

Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks.

Audio Classification Deep Attention +2

Training Binary Weight Networks via Semi-Binary Decomposition

no code implementations ECCV 2018 Qinghao Hu, Gang Li, Peisong Wang, Yifan Zhang, Jian Cheng

In this paper, we propose a novel semi-binary decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix.

Computational Efficiency

Two-Step Quantization for Low-Bit Neural Networks

1 code implementation CVPR 2018 Peisong Wang, Qinghao Hu, Yifan Zhang, Chunjie Zhang, Yang Liu, Jian Cheng

In this paper, we propose a simple yet effective Two-Step Quantization (TSQ) framework, by decomposing the network quantization problem into two steps: code learning and transformation function learning based on the learned codes.

Quantization Vocal Bursts Valence Prediction

From Hashing to CNNs: Training BinaryWeight Networks via Hashing

no code implementations8 Feb 2018 Qinghao Hu, Peisong Wang, Jian Cheng

To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing.

Recent Advances in Efficient Computation of Deep Convolutional Neural Networks

no code implementations3 Feb 2018 Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu

As for hardware implementation of deep neural networks, a batch of accelerators based on FPGA/ASIC have been proposed in recent years.

Network Pruning Quantization

Fixed-point Factorized Networks

no code implementations CVPR 2017 Peisong Wang, Jian Cheng

In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision.

General Classification

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