Search Results for author: Zhi Wang

Found 76 papers, 26 papers with code

Large Language Model Adaptation for Networking

no code implementations4 Feb 2024 Duo Wu, Xianda Wang, Yaqi Qiao, Zhi Wang, Junchen Jiang, Shuguang Cui, Fangxin Wang

In this paper, we present NetLLM, the first LLM adaptation framework that efficiently adapts LLMs to solve networking problems.

Answer Generation Language Modelling +3

Retraining-free Model Quantization via One-Shot Weight-Coupling Learning

no code implementations3 Jan 2024 Chen Tang, Yuan Meng, Jiacheng Jiang, Shuzhao Xie, Rongwei Lu, Xinzhu Ma, Zhi Wang, Wenwu Zhu

Conversely, mixed-precision quantization (MPQ) is advocated to compress the model effectively by allocating heterogeneous bit-width for layers.

Model Compression Quantization

OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments

no code implementations19 Dec 2023 Jinyi Liu, Zhi Wang, Yan Zheng, Jianye Hao, Chenjia Bai, Junjie Ye, Zhen Wang, Haiyin Piao, Yang Sun

In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.

Continuous Control

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

1 code implementation17 Dec 2023 Yaohua Zha, Huizhen Ji, Jinmin Li, Rongsheng Li, Tao Dai, Bin Chen, Zhi Wang, Shu-Tao Xia

Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct.

Few-Shot 3D Point Cloud Classification

Mixed Pseudo Labels for Semi-Supervised Object Detection

1 code implementation12 Dec 2023 Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang

While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach.

 Ranked #1 on Semi-Supervised Object Detection on COCO 100% labeled data (using extra training data)

Object object-detection +3

Unified learning-based lossy and lossless JPEG recompression

no code implementations5 Dec 2023 Jianghui Zhang, Yuanyuan Wang, Lina Guo, Jixiang Luo, Tongda Xu, Yan Wang, Zhi Wang, Hongwei Qin

Most image compression algorithms only consider uncompressed original image, while ignoring a large number of already existing JPEG images.

Image Compression Quantization

DAGC: Data-Volume-Aware Adaptive Sparsification Gradient Compression for Distributed Machine Learning in Mobile Computing

no code implementations13 Nov 2023 Rongwei Lu, Yutong Jiang, Yinan Mao, Chen Tang, Bin Chen, Laizhong Cui, Zhi Wang

Assigning varying compression ratios to workers with distinct data distributions and volumes is thus a promising solution.

Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing

no code implementations19 Aug 2023 Jie Zhang, Pengcheng Shi, Zaiwang Gu, Yiyang Zhou, Zhi Wang

In this paper, we present Semantic-Human, a novel method that achieves both photorealistic details and viewpoint-consistent human parsing for the neural rendering of humans.

Denoising Human Parsing +2

GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization

1 code implementation ICCV 2023 Hao Fang, Bin Chen, Xuan Wang, Zhi Wang, Shu-Tao Xia

Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradients calculated from their local data to a central server.

Federated Learning Image Generation

One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

1 code implementation5 Aug 2023 Hang Guo, Tao Dai, Mingyan Zhu, Guanghao Meng, Bin Chen, Zhi Wang, Shu-Tao Xia

Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition.

Contrastive Learning Knowledge Distillation +2

BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

1 code implementation1 Aug 2023 Junyi Wang, Yuanyang Zhu, Zhi Wang, Yan Zheng, Jianye Hao, Chunlin Chen

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters).

Bilevel Optimization reinforcement-learning +1

A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising

no code implementations16 Jul 2023 Yiwen Shan, Dong Hu, Haoming Ding, Chunming Yang, Zhi Wang

However, those methods mostly ignore either the cross-channel difference or the spatial variation of noise, which limits their capacity in real world color image denoising.

Color Image Denoising Image Denoising

Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning

no code implementations16 Jul 2023 Hongyu Ding, Yuanze Tang, Qing Wu, Bo wang, Chunlin Chen, Zhi Wang

Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity.

reinforcement-learning Reinforcement Learning (RL)

Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding

no code implementations18 May 2023 Taolin Zhang, Sunan He, Dai Tao, Bin Chen, Zhi Wang, Shu-Tao Xia

In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.

Contrastive Learning Object +2

Knowledge Soft Integration for Multimodal Recommendation

no code implementations12 May 2023 Kai Ouyang, Chen Tang, Wenhao Zheng, Xiangjin Xie, Xuanji Xiao, Jian Dong, Hai-Tao Zheng, Zhi Wang

To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about.

Multimodal Recommendation Retrieval

Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning

no code implementations12 May 2023 Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, Zhi Wang, Chunlin Chen

In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition.

Multi-agent Reinforcement Learning Starcraft +1

Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers

no code implementations29 Mar 2023 Mingqing Wang, Jiawei Li, Zhenyang Li, Chengxiao Luo, Bin Chen, Shu-Tao Xia, Zhi Wang

In this work, the VQVAE focus on feature extraction and reconstruction of images, and the transformers fit the manifold and locate anomalies in the latent space.

Unsupervised Anomaly Detection

Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation

no code implementations21 Mar 2023 Fang Hu, Xuexue Sun, Ke Qing, Fenxi Xiao, Zhi Wang, Xiaolu Fan

In this paper, we introduce a novel semi-supervised cell segmentation method called Multi-Microscopic-view Cell semi-supervised Segmentation (MMCS), which can train cell segmentation models utilizing less labeled multi-posture cell images with different microscopy well.

Cell Segmentation Segmentation

ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices

1 code implementation ICCV 2023 Chen Tang, Li Lyna Zhang, Huiqiang Jiang, Jiahang Xu, Ting Cao, Quanlu Zhang, Yuqing Yang, Zhi Wang, Mao Yang

However, prior supernet training methods that rely on uniform sampling suffer from the gradient conflict issue: the sampled subnets can have vastly different model sizes (e. g., 50M vs. 2G FLOPs), leading to different optimization directions and inferior performance.

Neural Architecture Search

Efficient and Secure Federated Learning for Financial Applications

no code implementations15 Mar 2023 Tao Liu, Zhi Wang, Hui He, Liangliang Lin, Wei Shi, Ran An, Chenhao Li

Experiments show that under different Non-IID experiment settings, our method can reduce the upload communication cost to about 2. 9% to 18. 9% of the conventional federated learning algorithm when the sparse rate is 0. 01.

Federated Learning

TextIR: A Simple Framework for Text-based Editable Image Restoration

no code implementations28 Feb 2023 Yunpeng Bai, Cairong Wang, Shuzhao Xie, Chao Dong, Chun Yuan, Zhi Wang

We use the text-image feature compatibility of the CLIP to alleviate the difficulty of fusing text and image features.

Colorization Image Colorization +3

SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

no code implementations14 Feb 2023 Chen Tang, Kai Ouyang, Zenghao Chai, Yunpeng Bai, Yuan Meng, Zhi Wang, Wenwu Zhu

This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.


MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees

no code implementations15 Sep 2022 Zichuan Liu, Yuanyang Zhu, Zhi Wang, Yang Gao, Chunlin Chen

While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions.

Multi-agent Reinforcement Learning reinforcement-learning +3

Thompson Sampling for Robust Transfer in Multi-Task Bandits

1 code implementation17 Jun 2022 Zhi Wang, Chicheng Zhang, Kamalika Chaudhuri

We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments.

Multi-Task Learning Thompson Sampling

CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing

1 code implementation24 May 2022 Zhiwei Hao, Yong Luo, Zhi Wang, Han Hu, Jianping An

To tackle this challenge, we propose a framework termed collaborative data-free knowledge distillation via multi-level feature sharing (CDFKD-MFS), which consists of a multi-header student module, an asymmetric adversarial data-free KD module, and an attention-based aggregation module.

Data-free Knowledge Distillation

A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

no code implementations22 May 2022 Zhi Wang, Chunlin Chen, Daoyi Dong

We use a Dirichlet process mixture to model the non-stationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space.

reinforcement-learning Reinforcement Learning (RL) +1

SeqNet: An Efficient Neural Network for Automatic Malware Detection

no code implementations8 May 2022 Jiawei Xu, Wenxuan Fu, Haoyu Bu, Zhi Wang, Lingyun Ying

We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments.

Efficient Neural Network Feature Engineering +2

Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach

1 code implementation29 Apr 2022 Shuzhao Xie, Yuan Xue, Yifei Zhu, Zhi Wang

With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLaaS), to the public.

BIG-bench Machine Learning reinforcement-learning +1

Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

no code implementations21 Apr 2022 Chen Tang, Haoyu Zhai, Kai Ouyang, Zhi Wang, Yifei Zhu, Wenwu Zhu

We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level.


Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning

no code implementations16 Apr 2022 Jinmei Liu, Zhi Wang, Chunlin Chen, Daoyi Dong

Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal.

Continual Learning reinforcement-learning +1

Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

1 code implementation16 Mar 2022 Chen Tang, Kai Ouyang, Zhi Wang, Yifei Zhu, YaoWei Wang, Wen Ji, Wenwu Zhu

For example, MPQ search on ResNet18 with our indicators takes only 0. 06 s, which improves time efficiency exponentially compared to iterative search methods.


Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation

no code implementations6 Mar 2022 Donghan Xie, Zhi Wang, Chunlin Chen, Daoyi Dong

In this paper, we propose a new method based on local communication learning to tackle the multi-agent RL (MARL) challenge within a large number of agents coexisting.

Reinforcement Learning (RL) SMAC+ +2

Fully Self-Supervised Learning for Semantic Segmentation

no code implementations24 Feb 2022 YuAn Wang, Wei Zhuo, Yucong Li, Zhi Wang, Qi Ju, Wenwu Zhu

To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.

Clustering Segmentation +2

bert2BERT: Towards Reusable Pretrained Language Models

no code implementations ACL 2022 Cheng Chen, Yichun Yin, Lifeng Shang, Xin Jiang, Yujia Qin, Fengyu Wang, Zhi Wang, Xiao Chen, Zhiyuan Liu, Qun Liu

However, large language model pre-training costs intensive computational resources and most of the models are trained from scratch without reusing the existing pre-trained models, which is wasteful.

Language Modelling Large Language Model

A Survey on Temporal Sentence Grounding in Videos

no code implementations16 Sep 2021 Xiaohan Lan, Yitian Yuan, Xin Wang, Zhi Wang, Wenwu Zhu

In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols(i. e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.

Benchmarking Sentence +2

EvilModel: Hiding Malware Inside of Neural Network Models

1 code implementation19 Jul 2021 Zhi Wang, Chaoge Liu, Xiang Cui

In this paper, we present a new method to covertly and evasively deliver malware through a neural network model.

Online Continual Adaptation with Active Self-Training

no code implementations11 Jun 2021 Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu

Our theoretical results show that OSAMD can fast adapt to changing environments with active queries.

Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation

no code implementations24 Apr 2021 Cheng Chen, Yichun Yin, Lifeng Shang, Zhi Wang, Xin Jiang, Xiao Chen, Qun Liu

Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression.

Knowledge Distillation

Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction

no code implementations15 Apr 2021 Yuanyang Zhu, Zhi Wang, Chunlin Chen, Daoyi Dong

In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time.

Navigate reinforcement-learning +3

Deepfake Forensics via An Adversarial Game

1 code implementation25 Mar 2021 Zhi Wang, Yiwen Guo, WangMeng Zuo

In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities.

Classification DeepFake Detection +2

Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models

1 code implementation7 Mar 2021 Linghan Meng, Yanhui Li, Lin Chen, Zhi Wang, Di wu, Yuming Zhou, Baowen Xu

To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i. e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance.

Spin-orbit driven ferromagnetism at half moiré filling in magic-angle twisted bilayer graphene

no code implementations12 Feb 2021 Jiang-Xiazi Lin, Ya-Hui Zhang, Erin Morissette, Zhi Wang, Song Liu, Daniel Rhodes, K. Watanabe, T. Taniguchi, James Hone, J. I. A. Li

Strong electron correlation and spin-orbit coupling (SOC) provide two non-trivial threads to condensed matter physics.

Mesoscale and Nanoscale Physics Materials Science Strongly Correlated Electrons

A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric

no code implementations22 Jan 2021 Yitian Yuan, Xiaohan Lan, Xin Wang, Long Chen, Zhi Wang, Wenwu Zhu

All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV.

Benchmarking Sentence +1

Polymorphous density-functional description of paramagnetic phases of quantum magnets

no code implementations7 Jan 2021 Yufei Zhao, Qiushi Yao, PengFei Liu, Jingzhi Han, Zhi Wang, Qihang Liu

The kernel of the study of magnetic quantum materials focuses on the magnetic phase transitions, among which the most common phenomenon is the transition between low-temperature magnetic-ordered phase to high-temperature paramagnetic phase.

Materials Science

MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning

no code implementations1 Jan 2021 Jinyi Liu, Zhi Wang, Jianye Hao, Yan Zheng

Recently, the principle of optimism in the face of (aleatoric and epistemic) uncertainty has been utilized to design efficient exploration strategies for Reinforcement Learning (RL).

Efficient Exploration reinforcement-learning +1

Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

2 code implementations2 Nov 2020 Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Thibaut Very, Isabelle Dupays, Zhi Wang, Théo Jaffrelot Inizan, Frédéric Célerse, Pengyu Ren, Jay W. Ponder, Jean-Philip Piquemal

Perspectives toward the strong-scaling performance of our multi-node massive parallelization strategy, unsupervised adaptive sampling and large scale applicability of the Tinker-HP code in biophysics are discussed.

Computational Physics Distributed, Parallel, and Cluster Computing Mathematical Software Chemical Physics

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

1 code implementation29 Oct 2020 Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri

In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol.

Performance Evaluation and Modeling of Cryptographic Libraries for MPI Communications

1 code implementation13 Oct 2020 Abu Naser, Mehran Sadeghi Lahijani, Cong Wu, Mohsen Gavahi, Viet Tung Hoang, Zhi Wang, Xin Yuan

In order for High-Performance Computing (HPC) applications with data security requirements to execute in the public cloud, the cloud infrastructure must ensure the privacy and integrity of data.

Distributed, Parallel, and Cluster Computing Cryptography and Security

CryptMPI: A Fast Encrypted MPI Library

1 code implementation13 Oct 2020 Abu Naser, Cong Wu, Mehran Sadeghi Lahijani, Mohsen Gavahi, Viet Tung Hoang, Zhi Wang, Xin Yuan

The cloud infrastructure must provide security for High-Performance Computing (HPC) applications of sensitive data to execute in such an environment.

Distributed, Parallel, and Cluster Computing Cryptography and Security

Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

1 code implementation9 Oct 2020 Zhi Wang, Chunlin Chen, Daoyi Dong

Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment.

Incremental Learning Q-Learning +3

Neurodynamic TDOA localization with NLOS mitigation via maximum correntropy criterion

no code implementations14 Sep 2020 Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Junli Liang, Zhi Wang

In this paper, we exploit the maximum correntropy criterion (MCC) to robustify the traditional time-difference-of-arrival (TDOA) location estimator in the presence of non-line-of-sight (NLOS) propagation conditions.


Maximum correntropy criterion for robust TOA-based localization in NLOS environments

no code implementations13 Sep 2020 Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Zhi Wang

We investigate the problem of time-of-arrival (TOA) based localization under possible non-line-of-sight (NLOS) propagation conditions.

Subtask Analysis of Process Data Through a Predictive Model

no code implementations29 Aug 2020 Zhi Wang, Xueying Tang, Jingchen Liu, Zhiliang Ying

Response process data collected from human-computer interactive items contain rich information about respondents' behavioral patterns and cognitive processes.


Lifelong Incremental Reinforcement Learning with Online Bayesian Inference

1 code implementation28 Jul 2020 Zhi Wang, Chunlin Chen, Daoyi Dong

In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments.

Bayesian Inference Clustering +2

ProcData: An R Package for Process Data Analysis

1 code implementation9 Jun 2020 Xueying Tang, Susu Zhang, Zhi Wang, Jingchen Liu, Zhiliang Ying

In addition, several response process generators and a real dataset of response processes of the climate control item in the 2012 Programme for International Student Assessment are included in the package.

AdaCompress: Adaptive Compression for Online Computer Vision Services

1 code implementation17 Sep 2019 Hongshan Li, Yu Guo, Zhi Wang, Shu-Tao Xia, Wenwu Zhu

Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size.

Multimedia Image and Video Processing

Decentralized Federated Learning: A Segmented Gossip Approach

no code implementations21 Aug 2019 Chenghao Hu, Jingyan Jiang, Zhi Wang

The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data.

Federated Learning

Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images

no code implementations26 Jul 2019 Fei Yu, Jie Zhao, Yanjun Gong, Zhi Wang, Yuxi Li, Fan Yang, Bin Dong, Quanzheng Li, Li Zhang

Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.

Generative Adversarial Network Transfer Learning

A Pvalue-guided Anomaly Detection Approach Combining Multiple Heterogeneous Log Parser Algorithms on IIoT Systems

no code implementations5 Jul 2019 Xueshuo Xie, Zhi Wang, Xuhang Xiao, Lei Yang, Shenwei Huang, Tao Li

In this paper, we use blockchain to prevent logs from being tampered with and propose a pvalue-guided anomaly detection approach.

Cryptography and Security

Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach

no code implementations3 Jun 2019 Yue Xu, Wenjun Xu, Zhi Wang, Jia-Ru Lin, Shuguang Cui

Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way.

reinforcement-learning Reinforcement Learning (RL)

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

6 code implementations21 Apr 2018 Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai

To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.

Click-Through Rate Prediction Recommendation Systems +2

Still Hammerable and Exploitable: on the Effectiveness of Software-only Physical Kernel Isolation

no code implementations20 Feb 2018 Yueqiang Cheng, Zhi Zhang, Surya Nepal, Zhi Wang

The exploit is motivated by our key observation that the modern OSes have double-owned kernel buffers (e. g., video buffers) owned concurrently by the kernel and user domains.

Cryptography and Security

Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural Realization

no code implementations14 Dec 2016 Wenlong Mou, Zhi Wang, Li-Wei Wang

In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set of activated neuroids with stable size.

Hippocampus Memorization

Online Influence Maximization in Non-Stationary Social Networks

1 code implementation26 Apr 2016 Yixin Bao, Xiaoke Wang, Zhi Wang, Chuan Wu, Francis C. M. Lau

Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology.


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