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检索条件"主题词=Inference Algorithms"

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Latency Analysis of Multi-Exit Split Federated Learning via Testbed Implementation

Latency Analysis of Multi-Exit Split Federated Learning via ...

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hyelee Lim Atif Rizwan Minseok Choi Department of Electronic Engineering Kyung Hee University Yongin South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Federated Learning (FL) offers a decentralized approach to training machine learning models across distributed devices, addressing data privacy concerns and reducing communication overhead inherent in centralized learning methods. However, as deep neural networks (DNNs) increase in complexity, the memory limitations of client devices pose significant challenges, particularly in real-time applications. To overcome these challenges, Split Learning (SL) has been proposed, which partitions DNN models between client devices and a central server, thereby reducing the burden on clients while maintaining data privacy. This paper extends SL by integrating a multi-exit strategy, allowing for early inference on less complex data samples to further enhance inference speed and efficiency. We validate our approach using a real-world testbed that replicates actual communication environments, contrasting it with existing methods under network conditions. Our results demonstrate that the Multi-Exit Federated Split Learning (ME-FedSL) algorithm not only reduces training and inference times but also improves the feasibility of deploying complex DNN models in federated learning environments, particularly in scenarios with bandwidth constraints.

关键词： Training Data privacy Federated learning Refining Artificial neural networks Bandwidth Real-time systems inference algorithms Data models Servers

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Bayesian Deghosting Algorithm for Multiple Target Tracking

Bayesian Deghosting Algorithm for Multiple Target Tracking

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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Pavel Kulmon Czech Technical University in Prague Czech Republic ERA a.s. Pardubice Czech Republic

ISBN: (数字)9781728164229

ISBN: (纸本)9781728164236

This paper deals with bistatic track association in classical Frequency Modulation (FM) based Multi Static Primary Surveillance Radar (MSPSR). We formulate deghosting procedure as Bayesian inference of association matrix between bistatic tracks and targets as well as target positions. To do that, we formulate prior probability distribution for the association matrix and develop custom Monte Carlo Markov Chain (MCMC) sampler, which is necessary to solve such a hybrid inference problem. Using simulated data, we compare the performance of the proposed algorithm with two others and show its superior performance in such a setup. At the end of the paper, we also outline further research of the algorithm.

关键词： Target tracking Frequency modulation Radar Radar tracking inference algorithms Probability distribution Bayes methods

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Tracking of nonstationary EEG with the polynomial root perturbation

Tracking of nonstationary EEG with the polynomial root pertu...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： L. Patomaki J.P. Kaipio P.A. Karjalainen M. Juntunen Department of Applied Physics University of Kuopio Kuopio Finland

A method for estimation of the change point in event related desynchronization tests is presented. The method is based on tracking of a single system pole of the time-varying ARMA model. The pole is approximated using... 详细信息

关键词： Electroencephalography Polynomials Brain modeling Least squares approximation Eigenvalues and eigenfunctions inference algorithms Least squares methods Resonance light scattering Eyes Signal analysis

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Parallel Computing for mmWave Beam Tracking Based on LSTM and Adversarial Sparse Transformer

Parallel Computing for mmWave Beam Tracking Based on LSTM an...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Chaohui Xu Liqun Fu School of Informatics Xiamen University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The 5G New Radio (5G NR) standard proposes beam management and beam tracking mechanisms, aiming to solve the blockage and beam misalignment in millimeter-wave (mmWave) communication. However, these usually become unrealistic due to the excessive time overhead. To address this issue and minimize the need for frequent beam training in high-speed scenarios, we aim to find a high accuracy and low complexity method for beam tracking. In particular, we propose a deep learning (DL)-based beam tracking algorithm, in which long short-term memory (LSTM) is cascaded with sparse transformer. LSTM extracts user equipment (UE) movement features from the received signal vector, and the sparse transformer predicts the optimal beam for future time slots in parallel. By incorporating adversarial training and a specifically designed loss function, our algorithm can be extended to handle more complex scenarios. The experimental results show that our algorithm could improve the top-1 accuracy (top-1 acc) by 3.65% when the velocity of UE is 20 m/s, and the model parameter numbers and the giga floating-point operations per second (GFLOPs) are only 16.3% and 11.98% of the baseline. Our scheme significantly reduces the model parameters while effectively improving the accuracy of beam tracking.

关键词： Training Deep learning Accuracy Tracking Transformers Prediction algorithms Feature extraction inference algorithms Millimeter wave communication Long short term memory

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Gradient Adjustment for better Differentiable Binarization in scene text detection

Gradient Adjustment for better Differentiable Binarization i...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Qing Mu Shuqiang Wang Dept. of Information and Electrical Engineering Hebei University of Engineering Handan China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Text detection, which aims to locate text areas in images, is an important step in reading text in scene images and is essential for office automation, visual search, blind assistance, etc. The methods(models) based on Differentiable Binarization (DB) have gained wide attention in segmentation-based methods due to better performance via enabling thresholds for binarization during backpropagation and simplifying post-processing. Thresholds for binarization require supervision by probability masks and threshold masks, which are generated from the ground truth. Therefore, improving the performance of DB models (DBnet [1] or Dbnet++ [2]) can be achieved by adopting a more rational label generation preprocess, which would allow for better supervision. In this paper, we propose a module in label generation to generate a gradient adjustment mask named GAM as an indicator to make better Differentiable Binarization. We will explain the motivation for this approach and conduct experiments on different datasets and DB models to demonstrate their effectiveness and robustness.

关键词： Training Backpropagation Visualization Office automation Image segmentation Text detection Robustness inference algorithms Convolutional neural networks Artificial intelligence

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Deep multimodal embedding: Manipulating novel objects with point-clouds, language and trajectories

Deep multimodal embedding: Manipulating novel objects with p...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jaeyong Sung Ian Lenz Ashutosh Saxena Department of Computer Science Stanford University Department of Computer Science Cornell University

A robot operating in a real-world environment needs to perform reasoning over a variety of sensor modalities such as vision, language and motion trajectories. However, it is extremely challenging to manually design features relating such disparate modalities. In this work, we introduce an algorithm that learns to embed point-cloud, natural language, and manipulation trajectory data into a shared embedding space with a deep neural network. To learn semantically meaningful spaces throughout our network, we use a loss-based margin to bring embeddings of relevant pairs closer together while driving less-relevant cases from different modalities further apart. We use this both to pre-train its lower layers and fine-tune our final embedding space, leading to a more robust representation. We test our algorithm on the task of manipulating novel objects and appliances based on prior experience with other objects. On a large dataset, we achieve significant improvements in both accuracy and inference time over the previous state of the art. We also perform end-to-end experiments on a PR2 robot utilizing our learned embedding space.

关键词： Trajectory Natural languages Neural networks inference algorithms Machine learning Robot sensing systems

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Flake Aware Culprit Finding

Flake Aware Culprit Finding

引用

International Conference on Software Testing, Verification, and Validation, ICST

作者： Tim A. D. Henderson Bobby Dorward Eric Nickell Collin Johnston Avi Kondareddy Google LLC Mountain View California USA

When a change introduces a bug into a large software repository, there is often a delay between when the change is committed and when bug is detected. This is true even when the bug causes an existing test to fail! These delays are caused by resource constraints which prevent the organization from running all of the tests on every change. Due to the delay, a Continuous Integration system needs to locate buggy commits. Locating them is complicated by flaky tests that pass and fail non-deterministically. The flaky tests introduce noise into the CI system requiring costly reruns to determine if a failure was caused by a bad code change or caused by non-deterministic test behavior. This paper presents an algorithm, Flake Aware Culprit Finding, that locates buggy commits more accurately than a traditional bisection search. The algorithm is based on Bayesian inference and noisy binary search, utilizing prior information about which changes are most likely to contain the bug. A large scale empirical study was conducted at Google on 13,000+ test breakages. The study evaluates the accuracy and cost of the new algorithm versus a traditional deflaked bisection search.

关键词： Software testing Costs Computer bugs Software algorithms Organizations inference algorithms Software

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Evaluation of MapReduce for Gridding LIDAR Data

Evaluation of MapReduce for Gridding LIDAR Data

引用

IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Sriram Krishnan Chaitanya Baru Christopher Crosby UC San Diego La Jolla CA USA

The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using shared-nothing parallelism. In this paper, we investigate the use of MapReduce technology for a local gridding algorithm for the generation of Digital Elevation Models (DEM). The local gridding algorithm utilizes the elevation information from LIDAR (Light, Detection, and Ranging) measurements contained within a circular search area to compute the elevation of each grid cell. The method is data parallel, lending itself to implementation using the MapReduce model. Here, we compare our initial C++ implementation of the gridding algorithm to a MapReduce-based implementation, and present observations on the performance (in particular, price/performance) and the implementation complexity. We also discuss the applicability of MapReduce technologies for related applications.

关键词： Laser radar Digital elevation models Surface topography Programming Arrays Merging inference algorithms

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Identification of chaotic system using recurrent compensatory neuro-fuzzy systems

Identification of chaotic system using recurrent compensator...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Cheng-Hung Chen Chin-Teng Lin Department of Electrical and Control Engineering National Chiao Tung University Hsinchu Taiwan

In this paper, a recurrent compensatory neuro-fuzzy system (RCNFS) is proposed for identification and prediction. The compensatory-based fuzzy reasoning method is using adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic systems more adaptive and effective. The recurrent network is embedded in the RCNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. Also, an online learning algorithm is proposed to automatically construct the RCNFS. They are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Finally, the RCNFS is applied in several simulations. The simulation results of the dynamic system modeling have shown that 1) the RCNFS model converges quickly; 2) the RCNFS model requires a small number of tuning parameters; 3) the RCNFS model can solve the temporal problems and approximate a dynamic system.

关键词： Chaos Fuzzy neural networks Fuzzy reasoning Fuzzy logic Fuzzy systems Neurofeedback Partitioning algorithms Backpropagation algorithms Adaptive systems inference algorithms

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Value-density algorithms to handle transient overloads in scheduling

Value-density algorithms to handle transient overloads in sc...

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Euromicro Conference on Real-Time Systems

作者： D. Mosse M.E. Pollack Y. Ronen Department of Computer Science University of Pittsburgh Pittsburgh PA USA University of Pittsburgh Pittsburgh PA US Inference Inc. New York NY USA

Systems with timing constraints have become pervasive in several disciplines, such as real-time artificial intelligence, operating systems, operations research, and local area networks. Most of the work in real-time system scheduling deals with admission control algorithms to guarantee that accepted tasks will meet their deadlines. In this paper, we compare the different algorithms, and suggest a novel algorithm that subsumes the previous ones with respect to schedulability in the case where the system may suffer from transient overloads and where tasks have precedence constraints among them. We show how our algorithm works in allocating time to competing reasoning modules in dynamic environments.

关键词： Scheduling algorithm Processor scheduling Job shop scheduling Real time systems Vehicle dynamics Dynamic scheduling inference algorithms Timing Intelligent systems Operating systems

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