检索结果-内蒙古大学图书馆

An Online inference-Aided Incentive Framework for Information Elicitation Without Verification

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第4期41卷 1167-1185页

作者： Huang, Chao Yu, Haoran Huang, Jianwei Berry, Randall Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

We study the design of incentive mechanisms for the problem of information elicitation without verification (IEWV). In IEWV, a data requester seeks to design proper incentives to optimize the tradeoff between the quality of information (collected from distributed crowd workers) and the total cost of incentives (provided to crowd workers) without verifiable ground truth. While prior work often relies on sufficient knowledge of worker information, we study a scenario where the data requester cannot access workers' heterogeneous information quality and costs ex-ante. We propose a continuum-armed bandit-based incentive mechanism that dynamically learns the optimal reward level from workers' reported information. A key challenge is that the data requester cannot evaluate the workers' information quality without verification, which motivates the design of an inference algorithm. The inference problem is non-convex, yet we reformulate it as a bi-convex problem and derive an approximate solution with a performance guarantee, which ensures the effectiveness of our online reward design. We further enhance the inference algorithm using part of the workers' historical reports. We also propose a novel rule for the data requester to aggregate workers' solutions more effectively. We show that our mechanism achieves a sub-linear regret O similar to(T-1/2) and outperforms several celebrated benchmarks.

关键词： Costs Task analysis inference algorithms Robots Pricing Optimization Crowdsourcing Distributed learning incentive mechanism design information elicitation without verification game theory

来源：评论

学校读者我要写书评

暂无评论

On the Accuracy of Analog Neural Network inference Accelerators [Feature]

引用

IEEE CIRCUITS AND SYSTEMS MAGAZINE 2022年第4期22卷 26-48页

作者： Xiao, T. Patrick Ben Feinberg, Ben Bennett, Christopher H. Prabhakar, Venkatraman Saxena, Prashant Agrawal, Vineet Agarwal, Sapan Marinella, Matthew J. Sandia Natl Labs Livermore CA 94551 USA Infineon Technol El Segundo CA USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ USA

Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes nonvolatile memory arrays to both store weights and perform in situ analog computation inside the array. While prior work has explored the design space of analog accelerators to optimize performance and energy efficiency, there is seldom a rigorous evaluation of the accuracy of these accelerators. This work shows how architectural design decisions, particularly in mapping neural network parameters to analog memory cells, influence inference accuracy. When evaluated using ResNet50 on ImageNet, the resilience of the system to analog non-idealities-cell programming errors, analog-to-digital converter resolution, and array parasitic resistances-all improve when analog quantities in the hardware are made proportional to the numerical values in the network. Moreover, contrary to the assumptions of prior work, nearly equivalent resilience to cell imprecision can be achieved by fully storing weights as analog quantities, rather than spreading weight bits across multiple devices, often referred to as bit slicing. By exploiting proportionality, analog system designers have the freedom to match the precision of the hardware to the needs of the algorithm, rather than attempting to guarantee the same level of precision in the intermediate results as an equivalent digital accelerator. This ultimately results in an analog accelerator that is more accurate, more robust to analog errors, and more energy-efficient.

关键词： Analog memory Neural networks Programming inference algorithms Accelerators Energy efficiency Robustness

来源：评论

学校读者我要写书评

暂无评论

Fully-Echoed Q-Routing With Simulated Annealing inference for Flying Adhoc Networks

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第3期8卷 2223-2234页

作者： Rovira-Sugranes, Arnau Afghah, Fatemeh Qu, Junsuo Razi, Abolfazl No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA Xian Univ Posts & Telecommun Sch Automat Xian 710021 Peoples R China

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate, compared to the state of the art Q-routing protocols.

关键词： Routing Routing protocols Network topology Topology Simulated annealing inference algorithms Heuristic algorithms UAV networks learning-based routing Q-routing adaptive networking energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Explicitly Solvable Continuous-Time inference for Partially Observed Markov Processes

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 6232-6242页

作者： Chen, Daniel Strang, Alexander G. Eckford, Andrew W. Thomas, Peter J. Case Western Reserve Univ Dept Math Appl Math & Stat Cleveland OH 44106 USA Case Western Reserve Univ Dept Elect Control & Syst Engn Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Case Western Reserve Univ Dept Biol Cleveland OH 44106 USA Univ Chicago Dept Stat Chicago IL 60637 USA York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

Many natural and engineered systems can be modeled as discrete state Markov processes. Often, only a subset of states are directly observable. Inferring the conditional probability that a system occupies a particular hidden state, given the partial observation, is a problem with broad application. In this paper, we introduce a continuous-time formulation of the sum-product algorithm, which is a well-known discrete-time method for finding the hidden states' conditional probabilities, given a set of finite, discrete-time observations. From our new formulation, we can explicitly solve for the conditional probability of occupying any state, given the transition rates and observations within a finite time window. We apply our algorithm to a realistic model of the cystic fibrosis transmembrane conductance regulator (CFTR) protein for exact inference of the conditional occupancy probability, given a finite time series of partial observations.

关键词： Markov processes Signal processing algorithms Sum product algorithm inference algorithms Hidden Markov models Biological system modeling Message passing Biomedical signal processing differential equations hidden markov models inference algorithms

来源：评论

学校读者我要写书评

暂无评论

CANET: Quantized Neural Network inference With 8-bit Carry-Aware Accumulator

引用

IEEE ACCESS 2024年 12卷 38765-38772页

作者： Yang, Jingxuan Wang, Xiaoqin Jiang, Yiying Inst Microelect Chinese Acad Sci Chaoyang Beijing 100029 Peoples R China Univ Chinese Acad Sci Sch Integrated Circuits Huairou Beijing 101408 Peoples R China

Neural network quantization represents weights and activations with few bits, greatly reducing the overhead of multiplications. However, due to the recursive accumulation operations, high-precision accumulators are still required in multiply-accumulate (MAC) units to avoid overflow, incurring significant computational overhead. This constraint limits the efficient deployment of quantized NNs on resource-constrained platforms. To address this problem, we present a novel framework named CANET, which adapts the 8-bit quantized model to execute MAC operations with 8-bit accumulators. CANET not only employs 8-bit carry-aware accumulators to represent overflow data correctly, but also adaptively learns the optimal format per layer to minimize truncation errors. Meanwhile, a weight-oriented reordering method is developed to reduce the transfer length of the carry. CANET is evaluated on three networks in the ImageNet classification task, where comparable performance with state-of-the-art methods is realized. Finally, we implement the proposed architecture on a custom hardware platform, demonstrating a reduction of 40% in power and 49% in area compared with the MAC unit with 32-bit accumulators.

关键词： Quantization (signal) Arithmetic Hardware Finite wordlength effects inference algorithms Costs Training Convolutional neural networks quantization efficient inference low-precision accumulator

来源：评论

学校读者我要写书评

暂无评论

Robust Variational Bayesian inference for Direction-of-Arrival Estimation With Sparse Array

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第8期71卷 8591-8602页

作者： Liu, Ying Zhang, Zongyu Zhou, Chengwei Yan, Chenggang Shi, Zhiguo Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Direction-of-arrival estimation Estimation Sensor arrays Sensors Bayes methods inference algorithms Sparse matrices Direction-of-arrival estimation gain-phase error outlier sparse array variational Bayesian inference

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Video-Based AI inference Tasks in MEC-Assisted Augmented Reality Systems

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第2期9卷 479-493页

作者： Pan, Guangjin Zhang, Heng Xu, Shugong Zhang, Shunqing Chen, Xiaojing Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

The high computational complexity and energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. However, mobile edge computing (MEC) makes it possible to solve this problem. This paper considers the scene of completing video-based AI inference tasks in the MEC system. We formulate a mixed-integer nonlinear programming problem (MINLP) to reduce inference delays, energy consumption and to improve recognition accuracy. We give a simplified expression of the inference complexity model and accuracy model through derivation and experimentation. The problem is then solved iteratively by using alternating optimization. Specifically, by assuming that the offloading decision is given, the problem is decoupled into two sub-problems, i.e., the resource allocation problem for the devices set that completes the inference tasks locally, and that for the devices set that offloads tasks. For the problem of offloading decision optimization, we propose a Channel-Aware heuristic algorithm. To further reduce the complexity, we propose an alternating direction method of multipliers (ADMM) based distributed algorithm. The ADMM-based algorithm has a low computational complexity that grows linearly with the number of devices. Numerical experiments show the effectiveness of proposed algorithms. The trade-off relationship between delay, energy consumption, and accuracy is also analyzed.

关键词： Task analysis Delays Artificial intelligence Computational modeling Energy consumption Servers inference algorithms Mobile augmented reality edge intelligence mobile edge computing resource allocation

来源：评论

学校读者我要写书评

暂无评论

Quickest inference of Network Cascades With Noisy Information

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第4期69卷 2494-2522页

作者： Sridhar, Anirudh Poor, H. Vincent Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

We study the problem of estimating the source of a network cascade given a time series of noisy information about the spread. Initially, there is a single vertex affected by the cascade (the source) and the cascade spreads in discrete time steps across the network. Although the cascade evolution is hidden, one observes a noisy measurement of the evolution at each time step. Given this information, we aim to reliably estimate the cascade source as fast as possible. We investigate Bayesian and minimax formulations of the source estimation problem, and derive near-optimal estimators for simple cascade dynamics and network topologies. In the Bayesian setting, samples are taken until the error of the Bayes-optimal estimator falls below a threshold. For the minimax setting, we design a novel multi-hypothesis sequential probability ratio test. These optimal estimators require log log n/ log(k - 1) observations for a k-regular tree network, and (log n) ?+1 (1) observations for l-dimensional lattice. We then discuss conjectures on source estimation in general topologies. Finally, we provide simulations which validate our theoretical results on trees and lattices, and illustrate the effectiveness of our methods for estimating the sources of cascades on Erdos-Renyi graphs.

关键词： Estimation Noise measurement Lattices Topology Network topology Bayes methods Mathematical models Graph theory inference algorithms adaptive algorithms Bayesian methods minimax techniques

来源：评论

学校读者我要写书评

暂无评论

Visibility inference based on spatial knowledge representation from observe's perspective

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 1997年第3期12卷 191-202页

作者： Huang, PW Lin, PL PROVIDENCE UNIV DEPT COMP SCI & INFORMAT MANAGEMENT TAICHUNG TAIWAN

In this article, we propose a picture-based object-oriented spatial knowledge representation, called PCOS-string, for an environment seen from the observer's point of view. The PCOS-string representation preserves the spatial relationships among objects and thus can facilitate spatial reasoning especially from the observer's perspective. We also present a visibility inference algorithm based on the PCOS-string representation so that visibility of the objects in a given environment can be determined according to their spatial relationships. This visibility inference algorithm can handle both static and dynamic environments. Finally, we discuss the possibility of applying the PCOS-string and the associated visibility inference method to the mobile robot's path planning problem and virtual reality applications. (C) 1997 John Wiley & Sons, Inc.

关键词： IMAGE DATABASE-SYSTEMS STRINGS Environments virtual reality Knowledge Representation inference algorithms visibility Observers Perspective Cosmic strings spatial reasoning

来源：评论

学校读者我要写书评

暂无评论

Learning Nonnegative Factors From Tensor Data: Probabilistic Modeling and inference Algorithm

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 1792-1806页

作者： Cheng, Lei Tong, Xueke Wang, Shuai Wu, Yik-Chung Poor, H. Vincent Shenzhen Res Inst Big Data Shenzhen 518000 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

Tensor canonical polyadic decomposition (CPD) with nonnegative factor matrices, which extracts useful latent information from multidimensional data, has found wide-spread applications in various big data analytic tasks. Currently, the implementation of most existing algorithms needs the knowledge of tensor rank. However, this information is practically unknown and difficult to acquire. To address this issue, a probabilistic approach is taken in this paper. Different from previous works, this paper firstly introduces a sparsity-promoting nonnegative Gaussian-gamma prior, based on which a novel probabilistic model for the CPD problem with nonnegative and continuous factors is established. This probabilistic model further enables the derivation of an efficient inference algorithm that accurately learns the nonnegative factors from the tensor data, along with an integrated feature of automatic rank determination. Numerical results using synthetic data and real-world applications are presented to show the remarkable performance of the proposed algorithm.

关键词： Tensile stress Probabilistic logic Signal processing algorithms inference algorithms Data mining Computational modeling Data models Tensor decomposition nonnegative factors variational inference automatic rank determination

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：