Jian Tang

Jian Tang
Syracuse University | SU · Department of Electrical Engineering and Computer Science

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182
Publications
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Publications

Publications (182)
Chapter
Generative adversarial imitation learning (GAIL) learns an optimal policy by expert demonstrations from the environment with unknown reward functions. Different from existing works that studied the generalization of reward function classes or discriminator classes, we focus on policy classes. This paper investigates the generalization and computati...
Preprint
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently. In this paper, we propose a Label-Guided auxiliary training method for 3D object detection (LG3D), which serves as an auxiliary network to enhance the feature learning of existing 3D object detectors. Specifically, we propose...
Preprint
Humans have a remarkable ability to quickly and effectively learn new concepts in a continuous manner without forgetting old knowledge. Though deep learning has made tremendous successes on various computer vision tasks, it faces challenges for achieving such human-level intelligence. In this paper, we define a new problem called continual few-shot...
Article
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we...
Article
The convolutional neural network (CNN) has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Channel pruning is usually applied to reduce the model redundancy while preserving the network structure, such that the pruned network can be easily deployed in practice. However, exi...
Preprint
The raw depth image captured by the indoor depth sensor usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and limited distance range. The incomplete depth map burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed...
Preprint
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we...
Article
Soil temperature and moisture play a significant influence on vegetation and climate. Measurements of soil temperature and moisture can now be obtained through soil temperature and moisture sensors, but the measurements can only be done for a specified period of time in the past. Predicting soil temperature and moisture for the future is of signifi...
Preprint
Deep neural networks (DNNs) have become ubiquitous techniques in mobile and embedded systems for applications such as image/object recognition and classification. The trend of executing multiple DNNs simultaneously exacerbate the existing limitations of meeting stringent latency/accuracy requirements on resource constrained mobile devices. The prio...
Preprint
Full-text available
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks. However, most existing pruning approaches are driven by empirical heuristics and rarely consider the...
Article
Fine-grained instance segmentation is considerably more complicated and challenging than semantic segmentation. Most existing instance segmentation methods only focus on accuracy without paying much attention to inference latency, which, is critical to real-time applications, such as autonomous driving. In this paper, we aim to bridge the gap betwe...
Article
Mobile crowdsensing (MCS) by unmanned aerial vehicles (UAVs) servicing delay-sensitive applications becomes popular by navigating a group of UAVs to take advantage of their equipped high-precision sensors and durability for data collection in harsh environments. In this paper, we aim to simultaneously maximize collected data amount, geographical fa...
Article
With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing Deep Neural Network (DNN) models to empower mobile and edge devices with intelligence such that they can support attractive AI applications in a real-time manner. To leverage heterogeneou...
Preprint
Full-text available
Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. However, most existing approaches explicitly leverage the pose information extracted from the source images as a conditional input for the generative networks. Meanwhile, they usually focus on the visual...
Article
While Deep Reinforcement Learning has emerged as a de facto approach to many complex experience-driven networking problems, it remains challenging to deploy DRL into real systems. Due to the random exploration or half-trained deep neural networks during the online training process, the DRL agent may make unexpected decisions, which may lead to sy...
Article
Video anomaly detection is commonly used in many applications, such as security surveillance, and is very challenging. A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames...
Preprint
Full-text available
In deep model compression, the recent finding "Lottery Ticket Hypothesis" (LTH) (Frankle & Carbin, 2018) pointed out that there could exist a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance than the original dense network. However, it is not easy to observe s...
Article
Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demon...
Article
Cloud Radio Access Networks (CRANs) have become a key enabling technique for the next generation wireless communications. Resource allocation in CRANs still needs to be further improved to reach the objective of minimizing power consumption and meeting demands of wireless users over a long period. Inspired by the success of Deep Reinforcement Learn...
Article
Experience-driven networking has emerged as a new and highly effective approach for resource allocation in complex communication networks. Deep Reinforcement Learning (DRL) has been shown to be a useful technique for enabling experience-driven networking. In this paper, we focus on a practical and fundamental problem for experience-driven networkin...
Chapter
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limit...
Preprint
Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging. A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames i...
Preprint
Scale variance is one of the crucial challenges in multi-scale object detection. Early approaches address this problem by exploiting the image and feature pyramid, which raises suboptimal results with computation burden and constrains from inherent network structures. Pioneering works also propose multi-scale (i.e., multi-level and multi-branch) fe...
Preprint
The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure, such that the pruned network can be easily deployed in practice. Howev...
Chapter
Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. H...
Conference Paper
Full-text available
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuo...
Preprint
Full-text available
While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuo...
Preprint
Full-text available
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such str...
Article
Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight pruning is a straightforward and effective method. There are currently two mainstreams of pruning methods representing two extremes of pruning regularity: non-structured, fine-...
Conference Paper
Full-text available
Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such str...
Article
Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoCompress, an automatic structured pruning framework...
Article
Full-text available
Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users’ location privacy has been a central issue. The study of various location perturbation techniques, e.g. k-anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attent...
Preprint
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel computing architectures. However, both of the pruning dimensions and pruning methods lack un...
Preprint
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limit...
Article
The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data parallelization and...
Article
Video question answering (VideoQA) is a very important but challenging multimedia task, which automatically analyzes questions and videos and generates accurate answers. However, research on VideoQA is still in its infancy. In this article, we propose a novel memory augmented deep recurrent neural network (MA-DRNN) model for VideoQA, which features...
Preprint
Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight pruning is a straightforward and effective method. There are currently two mainstreams of pruning methods representing two extremes of pruning regularity: non-structured, fine-...
Article
The explosive increase of mobile devices with built-in sensors such as GPS, accelerometer, gyroscope and camera has made the design of mobile crowdsensing (MCS) applications possible, which create a new interface between humans and their surroundings. Until now, various MCS applications have been designed, where the task initiators (TIs) recruit mo...
Preprint
Full-text available
Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoSlim, an automatic structured pruning framework with...
Article
In this paper, we aim to design a fully-distributed control solution to navigate a group of unmanned aerial vehicles (UAVs), as the mobile Base Stations (BSs) to fly around a target area, to provide long-term communication coverage for the ground mobile users. Different from existing solutions that mainly solve the problem from optimization perspec...
Preprint
Weight pruning and weight quantization are two important categories of DNN model compression. Prior work on these techniques are mainly based on heuristics. A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-ar...
Article
In this paper, we aim to study networking problems from a whole new perspective by leveraging emerging deep learning, to develop an experience-driven approach, which enables a network or a protocol to learn the best way to control itself from its own experience (e.g, runtime statistics data), just as a human learns a skill. We present design, imple...
Article
Full-text available
This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progressi...
Conference Paper
Full-text available
Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computing based neural...
Chapter
The growth of cloud computing has spurred many entities, both small and large, to use cloud services in order to achieve cost savings. Public cloud computing has allowed for quick, dynamic scalability without much overhead or long‐term commitments. However, there are some disincentives to using cloud services, and one of the biggest is the inherent...
Article
Mobile crowd sensing is a new paradigm that enables smart mobile devices to collect and share various types of sensing data in urban environments. However, new challenges arise: one is how to evaluate the quality of data each user potentially capable of providing; another is how to allocate satisfactory yet profitable amount of reward to keep them...
Preprint
Full-text available
Deep neural networks (DNNs) although achieving human-level performance in many domains, have very large model size that hinders their broader applications on edge computing devices. Extensive research work have been conducted on DNN model compression or pruning. However, most of the previous work took heuristic approaches. This work proposes a prog...
Chapter
Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direc...
Preprint
Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demo...
Article
Deep Convolutional Neural Networks (DCNNs) are one of the most promising types of deep learning technique and have been recognized as the dominant approach for almost all recognition and detection tasks. The computation of DCNNs is highly computational and memory intensive for the large feature maps and neuron connections, and the performance highl...
Preprint
Full-text available
Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a learning model in an adversarial m...
Conference Paper
Traditional Internet routing is simple, scalable and robust, but cannot provide perfect QoS support due to the current completely distributed hop-by-hop routing architecture. Software defined networking (SDN) opens up the door to traffic engineering innovation and makes possible QoS routing with a broader picture of overall network resources. We fu...
Article
Full-text available
In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs), which deal with processing of unbounded streams of continuous data at scale distributedly in real or near-real time. A fundamental problem in a DSDPS is the scheduling problem with the objective of minimizing average end-to-end tuple processing time. A w...
Article
In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs) , which deal with processing of unbounded streams of continuous data at scale distributedly in real or near-real time. A fundamental problem in a DSDPS is the scheduling problem (i.e., assigning workload to workers/machines) with the objective of minimizi...