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检索条件"主题词=prediction algorithms"

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prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning

Prediction Guided Meta-Learning for Multi-Objective Reinforc...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Liu, Fei-Yu Qian, Chao Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781728183923

Many real-world control problems consist of several different, possibly conflicting, objectives, which require finding a high-quality set of policies that are optimal for different objective preferences. Extensive research mainly focused on how to obtain a high-quality approximated Pareto set of policies, while another important research direction studies how to adapt to new objective preferences quickly. In this paper, we propose a new multi-objective reinforcement learning (MORL) algorithm so-called PG-Meta-MORL for achieving both goals. PG-Meta-MORL frames MORL as a meta-learning problem and iteratively optimizes a meta-policy using multiple tasks with objective preferences selected based on a prediction model, which is trained to guide the optimization process towards best improving the quality of the current Pareto set of policies. The empirical results on several multi-objective continuous control problems show that PG-Meta-MORL can find a high-quality approximated Pareto set of policies, and meanwhile, the obtained meta-policy can be adapted well to new objective preferences using few-shot interactions with the environment.

关键词： Adaptation models Reinforcement learning Evolutionary computation Predictive models prediction algorithms Approximation algorithms Task analysis

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prediction Augmented Segment Routing 22

Prediction Augmented Segment Routing

引用

IEEE 22nd International Conference on High Performance Switching and Routing (IEEE HPSR)

作者： Kodialam, Murali Lakshman, T., V Nokia Bell Labs Murray Hills NJ 07974 USA

ISBN: (纸本)9781665440059

With the increasing success of machine learning based approaches for prediction problems, there has been recent effort in improving the performance of online algorithms by augmenting them with machine learning predictions. Since machine learning predictions typically do not offer any performance guarantees, the new approach has to take into consideration the possibility that the machine learning prediction can be inaccurate. The idea is to develop approaches that give good results when the prediction is accurate (consistency) while ensuring that the performance is still acceptable in the worst case, when the prediction is not accurate (robustness). Segment routing is now being widely deployed and used for traffic engineering in IP networks. The key idea in segment routing is to break up the routing path into segments to better control routing paths and improve network utilization. We consider the problem of designing the segments in a network to minimize congestion. This is typically done for a predicted traffic matrix. We use the ideas of consistency and robustness to design a parametrized algorithm that gives good performance when the actual traffic matrix is exactly the predicted traffic matrix (consistency) while giving good performance in the worst case if the actual traffic matrix deviates significantly from the predicted traffic matrix (robustness).

关键词： Machine learning algorithms Network topology Conferences Machine learning Switches Routing prediction algorithms

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Deep Learning Approach for Long-Term prediction of Electric Vehicle (EV) Charging Station Availability

Deep Learning Approach for Long-Term Prediction of Electric ...

引用

IEEE Intelligent Transportation Systems Conference (ITSC)

作者： Luo, Ruikang Zhang, Yicheng Zhou, Yao Chen, Hailin Yang, Le Yang, Jianfei Su, Rong Nanyang Technol Univ Continental NTU Corp Lab 50 Nanyang Ave Singapore 639798 Singapore ASTAR Inst Infocomm Res I2R Singapore 138632 Singapore Nanyang Technol Univ Nanyang Technopreneurship Ctr Singapore 639798 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Natl Univ Singapore Dept Elect & Comp Engn ECE Singapore 117583 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781728191423

Traffic prediction with high accuracy has significance towards traffic facilities scheduling, adaptive traffic control logic, even the urban economic development. EV charging station availability prediction is one of the traffic facilities usage forecasting challenges in the urban planning. With the accurate long-term prediction of each EV charging station availability, drivers could schedule the charging activities wisely and avoid range anxiety. Data driven models are commonly applied to deal with the similar questions. However, due to the complex charging station distribution with topological road network structure and time-variant charging station availability, many widely used algorithms, such as recurrent neural network (RNN), could only extract the pure time-series information during the model training. One direct effect is that the prediction accuracy decreases rapidly over time. The Spatial-Temporal Graph Convolutional Network (STGCN) that consists the Graph Convolutional Network (GCN) considering edge connectivity and the Gated Recurrent Unit (GRU) is proposed to process both spatial and temporal dependence of relevant transportation data in this paper. Then, the spatial-temporal model is deployed on a real testing dataset in Dundee City and experiments show a better performance for EV charging station availability forecasting over a long-term period compared with other baselines and the possibility of real-time application.

关键词： Training Recurrent neural networks Biological system modeling Roads Predictive models Charging stations prediction algorithms

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Towards Accurate 3D Human Motion prediction from Incomplete Observations

Towards Accurate 3D Human Motion Prediction from Incomplete ...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cui, Qiongjie Sun, Huaijiang Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the existing work is seriously limited by complete observation, that is, once the historical motion sequence is incomplete (with missing values), it can only produce unexpected predictions or even deformities. Furthermore, due to inevitable reasons such as occlusion and the lack of equipment precision, the incompleteness of motion data occurs frequently, which hinders the practical application of current algorithms. In this work, we first notice this challenging problem, i.e., how to generate high-fidelity human motion predictions from incomplete observations. To solve it, we propose a novel multi-task graph convolutional network (MT-GCN). Specifically, the model involves two branches, in which the primary task is to focus on forecasting future 3D human actions accurately, while the auxiliary one is to repair the missing value of the incomplete observation. Both of them are integrated into a unified framework to share the spatio-temporal representation, which improves the final performance of each collaboratively. On three large-scale datasets, for various data missing scenarios in the real world, extensive experiments demonstrate that our approach is consistently superior to the state-of-the-art methods in which the missing values from incomplete observations are not explicitly analyzed.

关键词： Graphics Computer vision Solid modeling Three-dimensional displays Predictive models Maintenance engineering prediction algorithms

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Green Edge Intelligence Scheme for Mobile Keyboard Emoji prediction

Green Edge Intelligence Scheme for Mobile Keyboard Emoji Pre...

引用

IEEE International Conference on Communications (ICC)

作者： Hou, Jianfeng Tang, Yinxu Huang, Xi Shao, Ziyu Yang, Yang ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728171227

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the energy limitations of smartphones, it is promising to consider deploying pre-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, given a limited connection capacity, a key issue under such a scheme lies in how each smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency (a.k.a. the model selection problem). Moreover, part of the system dynamics such as the accuracy and the latency of individual models are usually unknown a priori in practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for edge-assisted mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sublinear regret bound and energy efficiency with high accuracy and low latency.

关键词： System dynamics Simulation Keyboards Predictive models prediction algorithms Energy efficiency User experience

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An efficient bi-fidelity method for continuous simulation optimization 18

An efficient bi-fidelity method for continuous simulation op...

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IEEE 18th International Conference on Automation Science and Engineering (IEEE CASE)

作者： Wang, Gengchen Jin, Xiao Lee, Loo Hay Northeastern Univ Dept Informat Sci & Engn Shenyang Peoples R China Natl Univ Singapore C4NGL Singapore Singapore Natl Univ Singapore Fac Engn Singapore Singapore

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

We study optimization problems on a continuous decision space using the Ordinal Transformation framework. The original method was proposed to exploit the advantages of bi-fidelity simulation models, so observations from the low-fidelity can efficiently guide sampling on the high-fidelity model. However, this framework only worked on a discrete decision space. We extend the framework to continuous cases and through preliminary analysis, we show promising performance and present future research directions.

关键词： Computer aided software engineering Automation Computational modeling Clustering algorithms Predictive models prediction algorithms Numerical models

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Improving Online Performance prediction for Semantic Segmentation

Improving Online Performance Prediction for Semantic Segment...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Klingner, Marvin Baer, Andreas Mross, Marcel Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781665448994

In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i.e., during inference, which is of high importance in safety-critical applications such as autonomous driving. Here, many high-level decisions rely on such DNNs, which are usually evaluated offline, while their performance in online operation remains unknown. To solve this problem, we propose an improved online performance prediction scheme, building on a recently proposed concept of predicting the primary semantic segmentation task's performance. This can be achieved by evaluating the auxiliary task of monocular depth estimation with a measurement supplied by a LiDAR sensor and a subsequent regression to the semantic segmentation performance. In particular, we propose (i) sequential training methods for both tasks in a multi-task training setup, (ii) to share the encoder as well as parts of the decoder between both task's networks for improved efficiency, and (iii) a temporal statistics aggregation method, which significantly reduces the performance prediction error at the cost of a small algorithmic latency. Evaluation on the KITH dataset shows that all three aspects improve the performance prediction compared to previous approaches.

关键词： Training Laser radar Semantics Estimation Virtual reality prediction algorithms Decoding

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prediction Alzheimer's disease from MRI images using deep learning 12

Prediction Alzheimer's disease from MRI images using deep le...

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12th International Conference on Information and Communication Systems (ICICS)

作者： Mggdadi, Esraa Al-Aiad, Ahmad Al-Ayyad, Muhammad Saleh Darabseh, Alaa Jordan Univ Sci & Technol Hlth Informat Syst Irbid Jordan Al Ahliyya Amman Univ Biomed Engn Dept Amman Jordan LaGuardia Community Coll New York NY USA

ISBN: (纸本)9781665433518

Alzheimer's is one of the diseases that are the most publicized type of dementia. Alzheimer's disease will be born every 3 second the world. Previous research shows that early prediction of AD in the medical field for reduced cost of treatment and time of it. To this end, construct an efficient prediction system for AD, which is the goal of this paper, often reduces time to treatment, medical errors, and overall healthcare cost. We used Deep Learning to predict and diagnose AD and for this reason using python code in Colaboratory as platform environments. In particular, we used 2D CNN and vgg16 to achieve the research goal, we used experiments conducted on MRI images from Kaggle dataset. Our experiment achieved accuracy of 67.5% for 2D CNN algorithm, while the vgg16 algorithm achieved accuracy of 70.3%. We conclude by showing that deep learning can improve the prediction AD and using algorithm vgg16 is better than 2D CNN.

关键词： Deep learning Communication systems Magnetic resonance imaging Medical services prediction algorithms Alzheimer's disease Medical diagnostic imaging

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Driver Intent prediction with Barrier Functions

Driver Intent Prediction with Barrier Functions

引用

American Control Conference (ACC)

作者： Rahman, Yousaf Jankovic, Mrdjan Santillo, Mario Ford Motor Co Res & Adv Engn 2101 Village Rd Dearborn MI 48124 USA

ISBN: (纸本)9781665441971

Path planning and collision avoidance algorithms for driver assist systems and autonomous vehicles require predictions of the future locations of other vehicles. In the absence of V2X communication, driver intent prediction algorithms provide this information. Intent prediction algorithms generally fall into two categories: those based on vehicle dynamics models and filtering, and machine learning based approaches. Both approaches have advantages and drawbacks, but two disadvantages both approaches share is the relative computational complexity and the likelihood of false positives and negatives. In the literature, barrier functions (BF) are used to enhance safety by providing forward invariance of an admissible set. In this paper, BFs are used to determine driver intent in a comparatively efficient manner with quick detection particularly at high speeds, in a way where the chance of false positives and negatives can be significantly reduced.

关键词： Machine learning algorithms Change detection algorithms Heuristic algorithms prediction algorithms Path planning Safety Collision avoidance

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Mobility prediction-Based Wireless Edge Caching Using Deep Reinforcement Learning

Mobility Prediction-Based Wireless Edge Caching Using Deep R...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Cai, Yuekai Chen, Youjia Ding, Ming Cheng, Peng Li, Jun Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China CSIRO Data61 Canberra ACT Australia La Trobe Univ Sch Engn & Math Sci Melbourne Vic Australia Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

ISBN: (纸本)9781665443852

Content caching in the edge of wireless networks is a promising technology to reduce the backhaul traffic of duplicated data transmission. Its key issue lies in the accurate prediction of user requirements. Considering the pervasive movement of users in cellular networks, especially in small-cell networks, we propose a mobility prediction-based content caching replacement strategy in this paper. Note that the impact of unevenly distributed file popularity is much larger in small cells than in macrocells due to their small coverage, where the local file request profile does not match the global one. In more detail, the user location predicted by long short term memory (LSTM) is incorporated into the caching replacement algorithm based on a deep reinforcement learning (DRL) framework. Simulation results show that the mobility prediction brings significant performance improvement in terms of cache hit ratio (CHR) in various movement scenarios, especially for a more regular movement pattern of users. Moreover, the optimal CHR threshold in the proposed algorithm is analytically derived, and the performance impact of learning rate as well as the storage size is also well investigated.

关键词： Cellular networks Wireless networks Simulation Collaboration Reinforcement learning prediction algorithms Data communication

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