Author name cluster

Bo Yuan

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36 papers

2 author rows

IROS Conference 2025 Conference Paper

An Improved A-Star Algorithm for Path Planning in Robot-Assisted Long Bone Fracture Reduction

Qin Gao
Xiaoyong Wu
Jun Ding
Bo Yuan
Yujin Wang
Ruizhi Shu

Long bone fractures are common clinical conditions, yet the development of robot systems for closed reduction surgery remains in its early stages. The key challenge in this field is the lack of an efficient and precise path planning algorithm. To address this issue, this study proposes an improved A-star (A*) algorithm for path planning to enhance the accuracy and efficiency of fracture reduction. The algorithm begins by expanding a random node using the fundamental A* algorithm. An artificial potential field (APF) algorithm is then incorporated to optimize the generation of sample node and enhance obstacle avoidance. Additionally, a cylindrical bounding box method is employed for collision detection, and a B-spline curve is utilized to smooth the generated path. The experimental validation is conducted on a fracture reduction robot system, demonstrating that the optimized path achieves clinically acceptable accuracy, significantly enhancing the precision and reliability of the reduction procedure.

TMLR Journal 2025 Journal Article

Client-only Distributed Markov Chain Monte Carlo Sampling over a Network

Bo Yuan
Jiaojiao Fan
Jiaming Liang
Yongxin Chen

We aim to sample from a target $\exp\left(-\sum_{i=1}^n f_i(x|\mathcal{D}_i\right))$ where each client $f_i$ only has access to local data $\mathcal{D}_i$. We present a fully distributed Markov Chain Monte Carlo (MCMC) sampler that operates through client-to-client communication, eliminating the need for additional centralized servers. Unlike MCMC algorithms that rely on server-client structures, our proposed sampler is entirely distributed, enhancing security and robustness through decentralized communication. In contrast to limited decentralized algorithms arising from Langevin dynamics, our sampler utilizes blocked Gibbs sampling on an augmented distribution. Furthermore, we establish a non-asymptotic analysis of our sampler, employing innovative techniques. This study contributes to one of the initial analyses of the non-asymptotic behavior of a fully distributed sampler arising from Gibbs sampling.

IROS Conference 2025 Conference Paper

Contactless and Economical Chemical Reaction Platform Based on Ultrasonic Field

Yunsheng Li
Qiao Wang
Yuyan Liu
Bo Yuan
Zhuo Chen
Qiang Huang 0002
Tatsuo Arai
Xiaoming Liu 0007

Chemical reactions constitute a cornerstone of fundamental scientific inquiry, yet traditional methodologies and platforms are encumbered by excessive reagent and consumable demands. Emerging alternatives, such as microfluidic systems, while innovative, suffer from intricate fabrication processes and elevated costs associated with operator training. Other contemporary approaches face limitations including reagent compatibility constraints and prohibitively expensive instrumentation. To address these challenges, this study introduces a contactless chemical reaction platform leveraging an ultrasonic vortex field to achieve stable capture, microscale droplet transport, and sequential multi-droplet mixing without direct contact. This platform substantially reduces contamination risks, minimizes reagent and consumable usage, accommodates a broad spectrum of reagent types, and imposes minimal demands on operator expertise. Demonstrating robust performance in microdose reaction control, the system offers significant potential for advancing chemical research and its applications.

TMLR Journal 2025 Journal Article

DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models

Yang Sui
Huy Phan
Jinqi Xiao
Tianfang Zhang
Zijie Tang
Cong Shi
Yan Wang
Yingying Chen

In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content-generation tool. Very recently, some pioneering works have shown the vulnerability of the diffusion model against backdoor attacks, calling for in-depth analysis and investigation of the security challenges. In this paper, we explore the detectability of the poisoned noise input for the backdoored diffusion models, an important performance metric yet little explored in the existing works. Starting from the perspective of a defender, we first analyze the distribution discrepancy of the trigger pattern in the existing diffusion backdoor attacks. Based on this finding, we propose a trigger detection mechanism that can effectively identify the poisoned input noise. Then, from the attack side, we propose a backdoor attack strategy that can learn the unnoticeable trigger to evade our proposed detection scheme. Our empirical evaluations across various diffusion models and datasets demonstrate the effectiveness of the proposed trigger detection and detection-evading attack strategy. For trigger detection, our distribution discrepancy-based solution can achieve a 100% detection rate for the Trojan triggers used in the existing works. For evading trigger detection, our proposed stealthy trigger design approach performs end-to-end learning to make the distribution of poisoned noise input approach that of benign noise, enabling nearly 100% detection pass rate with very high attack and benign performance for the backdoored diffusion models.

NeurIPS Conference 2024 Conference Paper

BitsFusion: 1.99 bits Weight Quantization of Diffusion Model

Yang Sui
Yanyu Li
Anil Kag
Yerlan Idelbayev
Junli Cao
Ju Hu
Dhritiman Sagar
Bo Yuan

Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a huge number of parameters, resulting in a significantly large model size. Saving and transferring them is a major bottleneck for various applications, especially those running on resource-constrained devices. In this work, we develop a novel weight quantization method that quantizes the UNet from Stable Diffusion v1. 5 to $1. 99$ bits, achieving a model with $7. 9\times$ smaller size while exhibiting even better generation quality than the original one. Our approach includes several novel techniques, such as assigning optimal bits to each layer, initializing the quantized model for better performance, and improving the training strategy to dramatically reduce quantization error. Furthermore, we extensively evaluate our quantized model across various benchmark datasets and through human evaluation to demonstrate its superior generation quality.

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AAAI Conference 2024 Short Paper

Dual Mapping of 2D StyleGAN for 3D-Aware Image Generation and Manipulation (Student Abstract)

Zhuo Chen
Haimei Zhao
Chaoyue Wang
Bo Yuan
Xiu Li

3D-aware GANs successfully solve the problem of 3D-consistency generation and furthermore provide a 3D shape of the generated object. However, the application of the volume renderer disturbs the disentanglement of the latent space, which makes it difficult to manipulate 3D-aware GANs and lowers the image quality of style-based generators. In this work, we devise a dual-mapping framework to make the generated images of pretrained 2D StyleGAN consistent in 3D space. We utilize a tri-plane representation to estimate the 3D shape of the generated object and two mapping networks to bridge the latent space of StyleGAN and the 3D tri-plane space. Our method does not alter the parameters of the pretrained generator, which means the interpretability of latent space is preserved for various image manipulations. Experiments show that our method lifts the 3D awareness of pretrained 2D StyleGAN to 3D-aware GANs and outperforms the 3D-aware GANs in controllability and image quality.

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AAAI Conference 2024 Short Paper

STViT: Improving Self-Supervised Multi-Camera Depth Estimation with Spatial-Temporal Context and Adversarial Geometry Regularization (Student Abstract)

Zhuo Chen
Haimei Zhao
Bo Yuan
Xiu Li

Multi-camera depth estimation has recently garnered significant attention due to its substantial practical implications in the realm of autonomous driving. In this paper, we delve into the task of self-supervised multi-camera depth estimation and propose an innovative framework, STViT, featuring several noteworthy enhancements: 1) we propose a Spatial-Temporal Transformer to comprehensively exploit both local connectivity and the global context of image features, meanwhile learning enriched spatial-temporal cross-view correlations to recover 3D geometry. 2) to alleviate the severe effect of adverse conditions, e.g., rainy weather and nighttime driving, we introduce a GAN-based Adversarial Geometry Regularization Module (AGR) to further constrain the depth estimation with unpaired normal-condition depth maps and prevent the model from being incorrectly trained. Experiments on challenging autonomous driving datasets Nuscenes and DDAD show that our method achieves state-of-the-art performance.

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AAAI Conference 2023 Conference Paper

CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness

Huy Phan
Miao Yin
Yang Sui
Bo Yuan
Saman Zonouz

Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks. However, the structured sparse models obtained by the existing works suffer severe performance degradation for both benign and robust accuracy, thereby causing a challenging dilemma between robustness and structuredness of compact DNNs. To address this problem, in this paper, we propose CSTAR, an efficient solution that simultaneously impose Compactness, high STructuredness and high Adversarial Robustness on the target DNN models. By formulating the structuredness and robustness requirement within the same framework, the compressed DNNs can simultaneously achieve high compression performance and strong adversarial robustness. Evaluations for various DNN models on different datasets demonstrate the effectiveness of CSTAR. Compared with the state-of-the-art robust structured pruning, CSTAR shows consistently better performance. For instance, when compressing ResNet-18 on CIFAR-10, CSTAR achieves up to 20.07% and 11.91% improvement for benign accuracy and robust accuracy, respectively. For compressing ResNet-18 with 16x compression ratio on Imagenet, CSTAR obtains 8.58% benign accuracy gain and 4.27% robust accuracy gain compared to the existing robust structured pruning.

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TMLR Journal 2023 Journal Article

Dynamics Adapted Imitation Learning

Zixuan Liu
Liu Liu
Bingzhe Wu
Lanqing Li
Xueqian Wang
Bo Yuan
Peilin Zhao

We consider Imitation Learning with dynamics variation between the expert demonstration (source domain) and the environment (target domain). Based on the popular framework of Adversarial Imitation Learning, we propose a novel algorithm – Dynamics Adapted Imitation Learning (DYNAIL), which incorporates the dynamics variation into the state-action occupancy measure matching as a regularization term. The dynamics variation is modeled by a pair of classifiers to distinguish between source dynamics and target dynamics. Theoretically, we provide an upper bound on the divergence between the learned policy and expert demonstrations in the source domain. Our error bound only depends on the expectation of the discrepancy between the source and target dynamics for the optimal policy in the target domain. The experiment evaluation validates that our method achieves superior results on high dimensional continuous control tasks, compared to existing imitation learning methods

AAAI Conference 2023 Conference Paper

Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks

Linrui Zhang
Qin Zhang
Li Shen
Bo Yuan
Xueqian Wang
Dacheng Tao

Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics. In this paper, we revisit prior work in this scope from the perspective of state-wise safe RL and categorize them as projection-based, recovery-based, and optimization-based approaches, respectively. Furthermore, we propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. This novel technique explicitly enforces hard constraints via the deep unrolling architecture and enjoys structural advantages in navigating the trade-off between reward improvement and constraint satisfaction. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit, a toolkit that provides off-the-shelf interfaces and evaluation utilities for safety-critical tasks. We then perform a comparative study of the involved algorithms on six benchmarks ranging from robotic control to autonomous driving. The empirical results provide an insight into their applicability and robustness in learning zero-cost-return policies without task-dependent handcrafting. The project page is available at https://sites.google.com/view/saferlkit.

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AAAI Conference 2023 Conference Paper

GOHSP: A Unified Framework of Graph and Optimization-Based Heterogeneous Structured Pruning for Vision Transformer

Miao Yin
Burak Uzkent
Yilin Shen
Hongxia Jin
Bo Yuan

The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foundation model. However, ViTs are typically constructed with large-scale sizes, which then severely hinder their potential deployment in many practical resources constrained applications. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. However, unlike its current popularity for CNNs and RNNs, structured pruning for ViT models is little explored. In this paper, we propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models. We first develop a graph-based ranking for measuring the importance of attention heads, and the extracted importance information is further integrated to an optimization-based procedure to impose the heterogeneous structured sparsity patterns on the ViT models. Experimental results show that our proposed GOHSP demonstrates excellent compression performance. On CIFAR-10 dataset, our approach can bring 40% parameters reduction with no accuracy loss for ViT-Small model. On ImageNet dataset, with 30% and 35% sparsity ratio for DeiT-Tiny and DeiT-Small models, our approach achieves 1.65% and 0.76% accuracy increase over the existing structured pruning methods, respectively.

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NeurIPS Conference 2023 Conference Paper

GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search

Xiao Zang
Miao Yin
Jinqi Xiao
Saman Zonouz
Bo Yuan

Motion planning, which aims to find a high-quality collision-free path in the configuration space, is a fundamental task in robotic systems. Recently, learning-based motion planners, especially the graph neural network-powered, have shown promising planning performance. However, though the state-of-the-art GNN planner can efficiently extract and learn graph information, its inherent mechanism is not well suited for graph search process, hindering its further performance improvement. To address this challenge and fully unleash the potential of GNN in motion planning, this paper proposes GraphMP, a neural motion planner for both low and high-dimensional planning tasks. With the customized model architecture and training mechanism design, GraphMP can simultaneously perform efficient graph pattern extraction and graph search processing, leading to strong planning performance. Experiments on a variety of environments, ranging from 2D Maze to 14D dual KUKA robotic arm, show that our proposed GraphMP achieves significant improvement on path quality and planning speed over the state-of-the-art learning-based and classical planners; while preserving the competitive success rate.

AAAI Conference 2023 Conference Paper

HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks

Jinqi Xiao
Chengming Zhang
Yu Gong
Miao Yin
Yang Sui
Lizhi Xiang
Dingwen Tao
Bo Yuan

Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model complexity and model accuracy, proper selection of layer-wise rank is very critical and desired. To date, though many low-rank compression approaches, either selecting the ranks in a manual or automatic way, have been proposed, they suffer from costly manual trials or unsatisfied compression performance. In addition, all of the existing works are not designed in a hardware-aware way, limiting the practical performance of the compressed models on real-world hardware platforms. To address these challenges, in this paper we propose HALOC, a hardware-aware automatic low-rank compression framework. By interpreting automatic rank selection from an architecture search perspective, we develop an end-to-end solution to determine the suitable layer-wise ranks in a differentiable and hardware-aware way. We further propose design principles and mitigation strategy to efficiently explore the rank space and reduce the potential interference problem. Experimental results on different datasets and hardware platforms demonstrate the effectiveness of our proposed approach. On CIFAR-10 dataset, HALOC enables 0.07% and 0.38% accuracy increase over the uncompressed ResNet-20 and VGG-16 models with 72.20% and 86.44% fewer FLOPs, respectively. On ImageNet dataset, HALOC achieves 0.9% higher top-1 accuracy than the original ResNet-18 model with 66.16% fewer FLOPs. HALOC also shows 0.66% higher top-1 accuracy increase than the state-of-the-art automatic low-rank compression solution with fewer computational and memory costs. In addition, HALOC demonstrates the practical speedups on different hardware platforms, verified by the measurement results on desktop GPU, embedded GPU and ASIC accelerator.

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TMLR Journal 2023 Journal Article

Replay-enhanced Continual Reinforcement Learning

Tiantian Zhang
Kevin Zehua Shen
Zichuan Lin
Bo Yuan
Xueqian Wang
Xiu Li
Deheng Ye

Replaying past experiences has proven to be a highly effective approach for averting catastrophic forgetting in supervised continual learning. However, some crucial factors are still largely ignored, making it vulnerable to serious failure, when used as a solution to forgetting in continual reinforcement learning, even in the context of perfect memory where all data of previous tasks are accessible in the current task. On the one hand, since most reinforcement learning algorithms are not invariant to the reward scale, the previously well-learned tasks (with high rewards) may appear to be more salient to the current learning process than the current task (with small initial rewards). This causes the agent to concentrate on those salient tasks at the expense of generality on the current task. On the other hand, offline learning on replayed tasks while learning a new task may induce a distributional shift between the dataset and the learned policy on old tasks, resulting in forgetting. In this paper, we introduce RECALL, a replay-enhanced method that greatly improves the plasticity of existing replay-based methods on new tasks while effectively avoiding the recurrence of catastrophic forgetting in continual reinforcement learning. RECALL leverages adaptive normalization on approximate targets and policy distillation on old tasks to enhance generality and stability, respectively. Extensive experiments on the Continual World benchmark show that RECALL performs significantly better than purely perfect memory replay, and achieves comparable or better overall performance against state-of-the-art continual learning methods.

EAAI Journal 2022 Journal Article

An evolutionary algorithm with indirect representation for droplet routing in digital microfluidic biochips

Chen Jiang
Rong-Quan Yang
Bo Yuan

As a revolutionary platform for miniaturizing laboratory procedures, the digital microfluidic biochip (DMFB) has the advantages of flexibility and re-configurability over its flow-based counterpart. Droplet routing is one of the most challenging problems in the design automation of DMFBs, which aims to schedule the movements of a set of droplets from their source electrodes to their target electrodes and satisfy both static and dynamic fluidic constraints. In this paper, we propose an evolutionary algorithm (EA) based droplet routing method with an indirect encoding scheme and an improved Dijkstra-based decoding strategy, to minimize the arrival time of the droplets. To be specific, the priority of the movements of the droplets are encoded in the chromosome instead of directly encoding the solution of the problem, i. e. , a complete path from the source to the target for each droplet. In the 2D-routing decoding stage, a problem-specific cost function is defined and introduced in the Dijkstra algorithm for obtaining a more time-efficient path for each droplet. Meanwhile, to avoid accidental mixing of the droplets during their movements, several strategies are proposed to modify the paths for satisfying the fluidic constraints in different scenarios of both 2D-routing and 3D-compaction. Compared with the state-of-the-art droplet routing algorithms, the experimental results demonstrate the superiority of the proposed method based on two synthetic benchmark suites and a real-world bioassay benchmark suite.

AAAI Conference 2022 Conference Paper

BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition

Miao Yin
Huy Phan
Xiao Zang
Siyu Liao
Bo Yuan

Model compression is very important for the efficient deployment of deep neural network (DNN) models on resourceconstrained devices. Among various model compression approaches, high-order tensor decomposition is particularly attractive and useful because the decomposed model is very small and fully structured. For this category of approaches, tensor ranks are the most important hyper-parameters that directly determine the architecture and task performance of the compressed DNN models. However, as an NP-hard problem, selecting optimal tensor ranks under the desired budget is very challenging and the state-of-the-art studies suffer from unsatisfied compression performance and timing-consuming search procedures. To systematically address this fundamental problem, in this paper we propose BATUDE, a Budget- Aware TUcker DEcomposition-based compression approach that can efficiently calculate optimal tensor ranks via oneshot training. By integrating the rank selecting procedure to the DNN training process with a specified compression budget, the tensor ranks of the DNN models are learned from the data and thereby bringing very significant improvement on both compression ratio and classification accuracy for the compressed models. The experimental results on ImageNet dataset show that our method enjoys 0. 33% top-5 higher accuracy with 2. 52× less computational cost as compared to the uncompressed ResNet-18 model. For ResNet-50, the proposed approach enables 0. 37% and 0. 55% top-5 accuracy increase with 2. 97× and 2. 04× computational cost reduction, respectively, over the uncompressed model.

IJCAI Conference 2022 Conference Paper

Don’t Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning

Zhecheng Yuan
Guozheng Ma
Yao Mu
Bo Xia
Bo Yuan
Xueqian Wang
Ping Luo
Huazhe Xu

One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at enhancing data diversity have demonstrated proven performance in improving the generalization ability of learned policies. However, due to the sensitivity of RL training, naively applying data augmentation, which transforms each pixel in a task-agnostic manner, may suffer from instability and damage the sample efficiency, thus further exacerbating the generalization performance. At the heart of this phenomenon is the diverged action distribution and high-variance value estimation in the face of augmented images. To alleviate this issue, we propose Task-aware Lipschitz Data Augmentation (TLDA) for visual RL, which explicitly identifies the task-correlated pixels with large Lipschitz constants, and only augments the task-irrelevant pixels for stability. We verify the effectiveness of our approach on DeepMind Control suite, CARLA and DeepMind Manipulation tasks. The extensive empirical results show that TLDA improves both sample efficiency and generalization; it outperforms previous state-of-the-art methods across 3 different visual control benchmarks.

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IJCAI Conference 2022 Conference Paper

Penalized Proximal Policy Optimization for Safe Reinforcement Learning

Linrui Zhang
Li Shen
Long Yang
Shixiang Chen
Xueqian Wang
Bo Yuan
Dacheng Tao

Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard constraint satisfaction. In this paper, we propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. Specifically, P3O utilizes a simple yet effective penalty approach to eliminate cost constraints and removes the trust-region constraint by the clipped surrogate objective. We theoretically prove the exactness of the penalized method with a finite penalty factor and provide a worst-case analysis for approximate error when evaluated on sample trajectories. Moreover, we extend P3O to more challenging multi-constraint and multi-agent scenarios which are less studied in previous work. Extensive experiments show that P3O outperforms state-of-the-art algorithms with respect to both reward improvement and constraint satisfaction on a set of constrained locomotive tasks.

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NeurIPS Conference 2022 Conference Paper

Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

Zhecheng Yuan
Zhengrong Xue
Bo Yuan
Xueqian Wang
Yi Wu
Yang Gao
Huazhe Xu

Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust representation via diversifying the appearances of in-domain observations for better generalization. Limited by the specific observations of the environment, these methods ignore the possibility of exploring diverse real-world image datasets. In this paper, we investigate how a visual RL agent would benefit from the off-the-shelf visual representations. Surprisingly, we find that the early layers in an ImageNet pre-trained ResNet model could provide rather generalizable representations for visual RL. Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner. Extensive experiments are conducted on DMControl Generalization Benchmark, DMControl Manipulation Tasks, Drawer World, and CARLA to verify the effectiveness of PIE-G. Empirical evidence suggests PIE-G improves sample efficiency and significantly outperforms previous state-of-the-art methods in terms of generalization performance. In particular, PIE-G boasts a 55% generalization performance gain on average in the challenging video background setting. Project Page: https: //sites. google. com/view/pie-g/home.

IROS Conference 2021 Conference Paper

A Novel 2-SUR 6-DOF Parallel Manipulator Actuated by Spherical Motion Generators

Kun Wang
Xiaoyong Wu
Yujin Wang
Bo Li
Bo Yuan
Shaoping Bai

A novel 6-DOF parallel manipulator with two spherical-universal-revolute limbs is proposed in this work. Compared with general 6-DOF parallel manipulators of six kinematic limbs, this new manipulator actuated by spherical motion generators has only two limbs, which brings kinematic advantages such as small footprint and large workspace. The inverse position problem of the manipulator is solved by an analytical approach, upon which velocity equations are formulated. Kinematic performance including workspace and also manipulability are calculated to show the advantages of the new design.

IJCAI Conference 2021 Conference Paper

Boosting Offline Reinforcement Learning with Residual Generative Modeling

Hua Wei
Deheng Ye
Zhao Liu
Hao Wu
Bo Yuan
Qiang Fu
Wei Yang
Zhenhui Li

Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) generative modeling, i. e. , approximating a policy using fixed data; and 2) learning the state-action value function. 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. In this paper, we analyze the error in generative modeling. We propose AQL (action-conditioned Q-learning), a residual generative model to reduce policy approximation error for offline RL. We show that our method can learn more accurate policy approximations in different benchmark datasets. In addition, we show that the proposed offline RL method can learn more competitive AI agents in complex control tasks under the multiplayer online battle arena (MOBA) game, Honor of Kings.

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NeurIPS Conference 2021 Conference Paper

CHIP: CHannel Independence-based Pruning for Compact Neural Networks

Yang Sui
Miao Yin
Yi Xie
Huy Phan
Saman Aliari Zonouz
Bo Yuan

Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-channel information. In this paper, starting from an inter-channel perspective, we propose to perform efficient filter pruning using Channel Independence, a metric that measures the correlations among different feature maps. The less independent feature map is interpreted as containing less useful information$/$knowledge, and hence its corresponding filter can be pruned without affecting model capacity. We systematically investigate the quantification metric, measuring scheme and sensitiveness$/$reliability of channel independence in the context of filter pruning. Our evaluation results for different models on various datasets show the superior performance of our approach. Notably, on CIFAR-10 dataset our solution can bring $0. 75\%$ and $0. 94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models, respectively, and meanwhile the model size and FLOPs are reduced by $42. 8\%$ and $47. 4\%$ (for ResNet-56) and $48. 3\%$ and $52. 1\%$ (for ResNet-110), respectively. On ImageNet dataset, our approach can achieve $40. 8\%$ and $44. 8\%$ storage and computation reductions, respectively, with $0. 15\%$ accuracy increase over the baseline ResNet-50 model. The code is available at https: //github. com/Eclipsess/CHIP_NeurIPS2021.

AAAI Conference 2021 Conference Paper

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

Siyu Liao
Chunhua Deng
Miao Yin
Bo Yuan

Recently deep neural networks have been successfully applied in channel coding to improve the decoding performance. However, the state-of-the-art neural channel decoders cannot achieve high decoding performance and low complexity simultaneously. To overcome this challenge, in this paper we propose doubly residual neural (DRN) decoder. By integrating both the residual input and residual learning to the design of neural channel decoder, DRN enables significant decoding performance improvement while maintaining low complexity. Extensive experiment results show that on different types of channel codes, our DRN decoder consistently outperform the state-of-the-art decoders in terms of decoding performance, model sizes and computational cost.

AAAI Conference 2021 Conference Paper

Enabling Fast and Universal Audio Adversarial Attack Using Generative Model

Yi Xie
Zhuohang Li
Cong Shi
Jian Liu
Yingying Chen
Bo Yuan

Recently, the vulnerability of deep neural network (DNN)based audio systems to adversarial attacks has obtained increasing attention. However, the existing audio adversarial attacks allow the adversary to possess the entire user’s audio input as well as granting sufficient time budget to generate the adversarial perturbations. These idealized assumptions, however, make 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). To overcome these limitations, in this paper we propose fast audio adversarial perturbation generator (FAPG), which uses generative model to generate adversarial perturbations for the audio input in a single forward pass, thereby drastically improving the perturbation generation speed. Built on the top of FAPG, we further propose universal audio adversarial perturbation generator (UAPG), a scheme to craft universal adversarial perturbation that can be imposed on arbitrary benign audio input to cause misclassification. Extensive experiments on DNN-based audio systems show that our proposed FAPG can achieve high success rate with up to 214× speedup over the existing audio adversarial attack methods. Also our proposed UAPG generates universal adversarial perturbations that can achieve much better attack performance than the state-of-the-art solutions.

IJCAI Conference 2021 Conference Paper

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

Xiao Zang
Yi Xie
Jie Chen
Bo Yuan

Deep neural networks, while generalize well, are known to be sensitive to small adversarial perturbations. This phenomenon poses severe security threat and calls for in-depth investigation of the robustness of deep learning models. With the emergence of neural networks for graph structured data, similar investigations are urged to understand their robustness. It has been found that adversarially perturbing the graph structure and/or node features may result in a significant degradation of the model performance. In this work, we show from a different angle that such fragility similarly occurs if the graph contains a few bad-actor nodes, which compromise a trained graph neural network through flipping the connections to any targeted victim. Worse, the bad actors found for one graph model severely compromise other models as well. We call the bad actors ``anchor nodes'' and propose an algorithm, named GUA, to identify them. Thorough empirical investigations suggest an interesting finding that the anchor nodes often belong to the same class; and they also corroborate the intuitive trade-off between the number of anchor nodes and the attack success rate. For the dataset Cora which contains 2708 nodes, as few as six anchor nodes will result in an attack success rate higher than 80% for GCN and other three models.

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AAAI Conference 2021 Conference Paper

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

Li Zhong
Zhen Fang
Feng Liu
Jie Lu
Bo Yuan
Guangquan Zhang

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: source risk, distribution discrepancy, and combined risk. Based on the assumption that the combined risk is a small fixed value, methods based on this bound train a target classifier by only minimizing estimators of the source risk and the distribution discrepancy. However, the combined risk may increase when minimizing both estimators, which makes the target risk uncontrollable. Hence the target classifier cannot achieve ideal performance if we fail to control the combined risk. To control the combined risk, the key challenge takes root in the unavailability of the labeled samples in the target domain. To address this key challenge, we propose a method named E-MixNet. E-MixNet employs enhanced mixup, a generic vicinal distribution, on the labeled source samples and pseudo-labeled target samples to calculate a proxy of the combined risk. Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy. Furthermore, we show that if the proxy of the combined risk is added into loss functions of four representative UDA methods, their performance is also improved.

IJCAI Conference 2020 Conference Paper

A Dual Input-aware Factorization Machine for CTR Prediction

Wantong Lu
Yantao Yu
Yongzhe Chang
Zhen Wang
Chenhui Li
Bo Yuan

Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model’s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.

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AAAI Conference 2020 Conference Paper

CAG: A Real-Time Low-Cost Enhanced-Robustness High-Transferability Content-Aware Adversarial Attack Generator

Huy Phan
Yi Xie
Siyu Liao
Jie Chen
Bo Yuan

Deep neural networks (DNNs) are vulnerable to adversarial attack despite their tremendous success in many artiﬁcial intelligence ﬁelds. Adversarial attack is a method that causes the intended misclassﬁcation by adding imperceptible perturbations to legitimate inputs. To date, researchers have developed numerous types of adversarial attack methods. However, from the perspective of practical deployment, these methods suffer from several drawbacks such as long attack generating time, high memory cost, insufﬁcient robustness and low transferability. To address the drawbacks, we propose a Content-aware Adversarial Attack Generator (CAG) to achieve real-time, low-cost, enhanced-robustness and hightransferability adversarial attack. First, as a type of generative model-based attack, CAG shows signiﬁcant speedup (at least 500 times) in generating adversarial examples compared to the state-of-the-art attacks such as PGD and C&W. Furthermore, CAG only needs a single generative model to perform targeted attack to any targeted class. Because CAG encodes the label information into a trainable embedding layer, it differs from prior generative model-based adversarial attacks that use n different copies of generative models for n different targeted classes. As a result, CAG signiﬁcantly reduces the required memory cost for generating adversarial examples. Moreover, CAG can generate adversarial perturbations that focus on the critical areas of input by integrating the class activation maps information in the training process, and hence improve the robustness of CAG attack against the state-of-art adversarial defenses. 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. Extensive experiments on different datasets and DNN models have veriﬁed the realtime, low-cost, enhanced-robustness, and high-transferability beneﬁts of CAG.

IJCAI Conference 2020 Conference Paper

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

Yiyang Zhang
Feng Liu
Zhen Fang
Bo Yuan
Guangquan Zhang
Jie Lu

In unsupervised domain adaptation (UDA), classifiers for the target domain are trained with massive true-label data from the source domain and unlabeled data from the target domain. However, it may be difficult to collect fully-true-label data in a source domain given limited budget. 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). The key benefit is that it is much less costly to collect complementary-label source data (required by BFUDA) than collecting the true-label source data (required by ordinary UDA). To this end, complementary label adversarial network (CLARINET) is proposed to solve the BFUDA problem. CLARINET maintains two deep networks simultaneously, where one focuses on classifying complementary-label source data and the other takes care of the source-to-target distributional adaptation. Experiments show that CLARINET significantly outperforms a series of competent baselines.

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IJCAI Conference 2020 Conference Paper

Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos

Haimei Zhao
Wei Bian
Bo Yuan
Dacheng Tao

Scene perceiving and understanding tasks including depth estimation, visual odometry (VO) and camera relocalization are fundamental for applications such as autonomous driving, robots and drones. Driven by the power of deep learning, significant progress has been achieved on individual tasks but the rich correlations among the three tasks are largely neglected. In previous studies, VO is generally accurate in local scope yet suffers from drift in long distances. By contrast, camera relocalization performs well in the global sense but lacks local precision. We argue that these two tasks should be strategically combined to leverage the complementary advantages, and be further improved by exploiting the 3D geometric information from depth data, which is also beneficial for depth estimation in turn. Therefore, we present a collaborative learning framework, consisting of DepthNet, LocalPoseNet and GlobalPoseNet with a joint optimization loss to estimate depth, VO and camera localization unitedly. Moreover, the Geometric Attention Guidance Model is introduced to exploit the geometric relevance among three branches during learning. Extensive experiments demonstrate that the joint learning scheme is useful for all tasks and our method outperforms current state-of-the-art techniques in depth estimation and camera relocalization with highly competitive performance in VO.

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AAAI Conference 2020 Conference Paper

Embedding Compression with Isotropic Iterative Quantization

Siyu Liao
Jie Chen
Yanzhi Wang
Qinru Qiu
Bo Yuan

Continuous representation of words is a standard component in deep learning-based NLP models. However, representing a large vocabulary requires signiﬁcant memory, which can cause problems, particularly on resource-constrained platforms. Therefore, in this paper we propose an isotropic iterative quantization (IIQ) approach for compressing embedding vectors into binary ones, leveraging the iterative quantization technique well established for image retrieval, while satisfying the desired isotropic property of PMI based models. Experiments with pre-trained embeddings (i. e. , GloVe and HDC) demonstrate a more than thirty-fold compression ratio with comparable and sometimes even improved performance over the original real-valued embedding vectors.

NeurIPS Conference 2020 Conference Paper

Towards Playing Full MOBA Games with Deep Reinforcement Learning

Deheng Ye
Guibin Chen
Wen Zhang
Sheng Chen
Bo Yuan
Bo Liu
Jia Chen
Zhao Liu

MOBA games, e. g. , Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. 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. As a result, full MOBA games without restrictions are far from being mastered by any existing AI system. In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning. Specifically, we develop a combination of novel and existing learning techniques, including off-policy adaption, multi-head value estimation, curriculum self-play learning, policy distillation, and Monte-Carlo tree-search, in training and playing a large pool of heroes, meanwhile addressing the scalability issue skillfully. Tested on Honor of Kings, a popular MOBA game, we show how to build superhuman AI agents that can defeat top esports players. The superiority of our AI is demonstrated by the first large-scale performance test of MOBA AI agent in the literature.

IJCAI Conference 2019 Conference Paper

An Input-aware Factorization Machine for Sparse Prediction

Yantao Yu
Zhen Wang
Bo Yuan

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents features using factorized parameters and weights. However, the accuracy of FMs can be adversely affected by the fixed representation trained for each feature, as the same feature is usually not equally predictive and useful in different instances. In fact, the inaccurate representation of features may even introduce noise and degrade the overall performance. In this work, we improve FMs by explicitly considering the impact of individual input upon the representation of features. We propose a novel model named \textit{Input-aware Factorization Machine} (IFM), which learns a unique input-aware factor for the same feature in different instances via a neural network. Comprehensive experiments on three real-world recommendation datasets are used to demonstrate the effectiveness and mechanism of IFM. Empirical results indicate that IFM is significantly better than the standard FM model and consistently outperforms four state-of-the-art deep learning based methods.

AAAI Conference 2019 Conference Paper

CircConv: A Structured Convolution with Low Complexity

Siyu Liao
Bo Yuan

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the large model sizes of DNNs yield high demands on computation resource and weight storage, thereby limiting the practical deployment of DNNs. To overcome these limitations, this paper proposes to impose the circulant structure to the construction of convolutional layers, and hence leads to circulant convolutional layers (CircConvs) and circulant CNNs. The circulant structure and models can be either trained from scratch or re-trained from a pre-trained non-circulant model, thereby making it very flexible for different training environments. Through extensive experiments, such strong structureimposing approach is proved to be able to substantially reduce the number of parameters of convolutional layers and enable significant saving of computational cost by using fast multiplication of the circulant tensor.

AAAI Conference 2019 Conference Paper

Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks

Yanzhi Wang
Zheng Zhan
Liang Zhao
Jian Tang
Siyue Wang
Jiayu Li
Bo Yuan
Wujie Wen

Large-scale deep neural networks are both memory and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated. Specific forms of binary neural networks (BNNs) and stochastic computing-based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations. Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the ”ideal” SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior), which is a new angle from the original approximation property. The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a “bridge” to prove for BNNs. Besides the universal approximation property, we also derive an appropriate bound for bit length M in order to provide insights for the actual neural network implementations. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growth of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SC- NNs are more suitable.

AAAI Conference 2018 Conference Paper

Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Yanzhi Wang
Caiwen Ding
Zhe Li
Geng Yuan
Siyu Liao
Xiaolong Ma
Bo Yuan
Xuehai Qian

Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efﬁciency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which is applicable to different DNN types, sizes, and application scenarios. The algorithm part adopts the general block-circulant matrices to achieve a ﬁne-grained tradeoff of accuracy and compression ratio. It applies to both fully-connected and convolutional layers and contains a mathematically rigorous proof of the effectiveness of the method. The proposed algorithm reduces computational complexity per layer from O(n2 ) to O(n log n) and storage complexity from O(n2 ) to O(n), both for training and inference. The hardware part consists of highly efﬁcient Field Programmable Gate Array (FPGA)-based implementations using effective reconﬁguration, batch processing, deep pipelining, resource re-using, and hierarchical control. Experimental results demonstrate that the proposed framework achieves at least 152X speedup and 71X energy efﬁciency gain compared with IBM TrueNorth processor under the same test accuracy. It achieves at least 31X energy efﬁciency gain compared with the reference FPGA-based work.