Search Results for author: Bo Yuan

Found 50 papers, 7 papers with code

Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation

no code implementations5 Apr 2022 Bo Yuan, Danpei Zhao, Shuai Shao, Zehuan Yuan, Changhu Wang

In two typical cross-domain semantic segmentation tasks, i. e., GTA5 to Cityscapes and SYNTHIA to Cityscapes, our method achieves the state-of-the-art segmentation accuracy.

Semantic Segmentation Unsupervised Domain Adaptation

Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism

no code implementations3 Jan 2022 Yunhui Zeng, Zijun Liao, Yuanzhi Dai, Rong Wang, Xiu Li, Bo Yuan

The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings.

Graph Representation Learning reinforcement-learning

A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning

no code implementations1 Jan 2022 Yuxing Wang, Tiantian Zhang, Yongzhe Chang, Bin Liang, Xueqian Wang, Bo Yuan

The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms.

Continuous Control OpenAI Gym +1

Probability Density Estimation Based Imitation Learning

no code implementations13 Dec 2021 Yang Liu, Yongzhe Chang, Shilei Jiang, Xueqian Wang, Bin Liang, Bo Yuan

In general, IL methods can be categorized into Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL).

Density Estimation Imitation Learning

An Open Source Representation for the NYS Electric Grid to Support Power Grid and Market Transition Studies

1 code implementation13 Dec 2021 M. Vivienne Liu, Bo Yuan, Zongjie Wang, Jeffrey A. Sward, K. Max Zhang, C. Lindsay Anderson

Under the increasing need to decarbonize energy systems, there is coupled acceleration in connection of distributed and intermittent renewable resources in power grids.

CHIP: CHannel Independence-based Pruning for Compact Neural Networks

1 code implementation NeurIPS 2021 Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Zonouz, Bo Yuan

Filter pruning has been widely used for neural network compression because of its enabled practical acceleration.

Neural Network Compression

SPARK: co-exploring model SPArsity and low-RanKness for compact neural networks

no code implementations29 Sep 2021 Wanzhao Yang, Miao Yin, Yang Sui, Bo Yuan

Based on the observations and outcomes from our analysis, we then propose SPARK, a unified DNN compression framework that can simultaneously capture model SPArsity and low-RanKness in an efficient way.

Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning

no code implementations6 Sep 2021 Yuxiang Sun, Bo Yuan, Yufan Xue, Jiawei Zhou, XiaoYu Zhang, Xianzhong Zhou

Researchers are increasingly focusing on intelligent games as a hot research area. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict. Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations.

Decision Making reinforcement-learning

Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation

no code implementations1 Sep 2021 Tiantian Zhang, Xueqian Wang, Bin Liang, Bo Yuan

In CDaKD, we exploit online clustering to achieve context division, and interference is further alleviated by a knowledge distillation regularization term on the output layers for learned contexts.

Knowledge Distillation Online Clustering +2

Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework

no code implementations CVPR 2021 Miao Yin, Yang Sui, Siyu Liao, Bo Yuan

Notably, on CIFAR-100, with 2. 3X and 2. 4X compression ratios, our models have 1. 96% and 2. 21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively.

Image Classification Model Compression +2

Boosting Offline Reinforcement Learning with Residual Generative Modeling

no code implementations19 Jun 2021 Hua Wei, Deheng Ye, Zhao Liu, Hao Wu, Bo Yuan, Qiang Fu, Wei Yang, Zhenhui Li

While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.

Offline RL Q-Learning +1

Optimization of Service Addition in Multilevel Index Model for Edge Computing

no code implementations8 Jun 2021 Jiayan Gu, Yan Wu, Ashiq Anjum, John Panneerselvam, Yao Lu, Bo Yuan

With the development of Edge Computing and Artificial Intelligence (AI) technologies, edge devices are witnessed to generate data at unprecedented volume.


Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

no code implementations1 May 2021 Chen Zhang, Siwei Wang, Wenxuan Tu, Pei Zhang, Xinwang Liu, Changwang Zhang, Bo Yuan

Multi-view clustering is an important yet challenging task in machine learning and data mining community.

Inference of cell dynamics on perturbation data using adjoint sensitivity

1 code implementation13 Apr 2021 Weiqi Ji, Bo Yuan, Ciyue Shen, Aviv Regev, Chris Sander, Sili Deng

While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability.

Towards Extremely Compact RNNs for Video Recognition with Fully Decomposed Hierarchical Tucker Structure

no code implementations CVPR 2021 Miao Yin, Siyu Liao, Xiao-Yang Liu, Xiaodong Wang, Bo Yuan

Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in resource-restricted environments is still a very challenging problem.

Tensor Decomposition Video Recognition

Noise Injection-based Regularization for Point Cloud Processing

no code implementations28 Mar 2021 Xiao Zang, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan

In this paper, we, for the first time, perform systematic investigation on noise injection-based regularization for point cloud-domain DNNs.

Data Augmentation Semantic Segmentation

Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding

no code implementations8 Feb 2021 Siyu Liao, Chunhua Deng, Miao Yin, Bo Yuan

Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance.

NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection

no code implementations8 Jan 2021 Liang Xu, Liying Zheng, Weijun Li, Zhenbo Chen, Weishun Song, Yue Deng, Yongzhe Chang, Jing Xiao, Bo Yuan

In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs).

Anomaly Detection Time Series

How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

no code implementations30 Dec 2020 Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang

Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy.

Unsupervised Domain Adaptation

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations25 Nov 2020 Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

Towards Playing Full MOBA Games with Deep Reinforcement Learning

no code implementations NeurIPS 2020 Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.

Dota 2 League of Legends +1

Learning from a Complementary-label Source Domain: Theory and Algorithms

1 code implementation4 Aug 2020 Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu

We consider two cases of this setting, one is that the source domain only contains complementary-label data (completely complementary unsupervised domain adaptation, CC-UDA), and the other is that the source domain has plenty of complementary-label data and a small amount of true-label data (partly complementary unsupervised domain adaptation, PC-UDA).

Unsupervised Domain Adaptation

Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation

1 code implementation29 Jul 2020 Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu

To mitigate this problem, we consider a novel problem setting where the classifier for the target domain has to be trained with complementary-label data from the source domain and unlabeled data from the target domain named budget-friendly UDA (BFUDA).

Unsupervised Domain Adaptation

Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density

no code implementations27 Jun 2020 Wei Wang, Gangqiang Hu, Bo Yuan, Shandong Ye, Chao Chen, YaYun Cui, Xi Zhang, Liting Qian

To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.

Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation

no code implementations23 Jun 2020 Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu

To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.

Domain Adaptation Object Recognition

Enabling Fast and Universal Audio Adversarial Attack Using Generative Model

no code implementations26 Apr 2020 Yi Xie, Zhuohang Li, Cong Shi, Jian Liu, Yingying Chen, Bo Yuan

These idealized assumptions, however, makes the existing audio adversarial attacks mostly impossible to be launched in a timely fashion in practice (e. g., playing unnoticeable adversarial perturbations along with user's streaming input).

Adversarial Attack

PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices

no code implementations23 Apr 2020 Chunhua Deng, Siyu Liao, Yi Xie, Keshab K. Parhi, Xuehai Qian, Bo Yuan

On the other hand, the recent structured matrix-based approach (i. e., CirCNN) is limited by the relatively complex arithmetic computation (i. e., FFT), less flexible compression ratio, and its inability to fully utilize input sparsity.

Model Compression

Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems

no code implementations4 Mar 2020 Yi Xie, Cong Shi, Zhuohang Li, Jian Liu, Yingying Chen, Bo Yuan

As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services.

Adversarial Attack Speaker Recognition

Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

1 code implementation12 Feb 2020 Xiao Zang, Yi Xie, Jie Chen, Bo Yuan

Worse, the bad actors found for one graph model severely compromise other models as well.

Graph Learning

Embedding Compression with Isotropic Iterative Quantization

no code implementations11 Jan 2020 Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, Bo Yuan

Continuous representation of words is a standard component in deep learning-based NLP models.

Image Retrieval Quantization

CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

no code implementations16 Dec 2019 Huy Phan, Yi Xie, Siyu Liao, Jie Chen, Bo Yuan

In addition, CAG exhibits high transferability across different DNN classifier models in black-box attack scenario by introducing random dropout in the process of generating perturbations.

Adversarial Attack

They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization

no code implementations16 Sep 2019 Rayan Mosli, Matthew Wright, Bo Yuan, Yin Pan

In this paper, we present AdversarialPSO, a black-box attack that uses fewer queries to create adversarial examples with high success rates.

Image Classification

A Critical Note on the Evaluation of Clustering Algorithms

no code implementations10 Aug 2019 Tiantian Zhang, Li Zhong, Bo Yuan

Experimental evaluation is a major research methodology for investigating clustering algorithms and many other machine learning algorithms.

Dimensionality Reduction

CircConv: A Structured Convolution with Low Complexity

no code implementations28 Feb 2019 Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, Bo Yuan

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications.

Representation Learning for Heterogeneous Information Networks via Embedding Events

1 code implementation29 Jan 2019 Guoji Fu, Bo Yuan, Qiqi Duan, Xin Yao

Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.

Link Prediction Node Classification +1

SGAD: Soft-Guided Adaptively-Dropped Neural Network

no code implementations4 Jul 2018 Zhisheng Wang, Fangxuan Sun, Jun Lin, Zhongfeng Wang, Bo Yuan

Based on the developed guideline and adaptive dropping mechanism, an innovative soft-guided adaptively-dropped (SGAD) neural network is proposed in this paper.

Model Compression

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

no code implementations10 May 2018 Zhe Li, Ji Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Qinru Qiu, Bo Yuan, Yanzhi Wang

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications.

Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs

no code implementations28 Mar 2018 Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang

For FPGA implementations on deep convolutional neural networks (DCNNs), we achieve at least 152X and 72X improvement in performance and energy efficiency, respectively using the SWM-based framework, compared with the baseline of IBM TrueNorth processor under same accuracy constraints using the data set of MNIST, SVHN, and CIFAR-10.

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

no code implementations14 Mar 2018 Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang

The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

no code implementations ICML 2017 Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Victor Pan, Bo Yuan

Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks.

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

no code implementations18 Nov 2016 Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

Stochastic Computing (SC), which uses bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has a high potential for implementing DCNNs with high scalability and ultra-low hardware footprint.

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