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Proceedings of the 37th International conference on Neural Information processing Systems

作者： Aleksandr Beznosikov Martin Takáč Alexander Gasnikov Innopolis University Skoltech MIPT Yandex MBZUAI MIPT Skoltech IITP RAS

Variational inequalities are a broad and flexible class of problems that includes minimization, saddle point, and fixed point problems as special cases. Therefore, variational inequalities are used in various applications ranging from equilibrium search to adversarial learning. With the increasing size of data and models, today's instances demand parallel and distributed computing for real-world machine learning problems, most of which can be represented as variational inequalities. Meanwhile, most distributed approaches have a significant bottleneck - the cost of communications. The three main techniques to reduce the total number of communication rounds and the cost of one such round are the similarity of local functions, compression of transmitted information, and local updates. In this paper, we combine all these approaches. Such a triple synergy did not exist before for variational inequalities and saddle problems, nor even for minimization problems. The methods presented in this paper have the best theoretical guarantees of communication complexity and are significantly ahead of other methods for distributed variational inequalities. The theoretical results are confirmed by adversarial learning experiments on synthetic and real datasets.

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3-D temperature field reconstruction of lithium-ion battery pack using real-time distributed moving horizon estimation

3-D temperature field reconstruction of lithium-ion battery ...

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第35届中国控制与决策会议

作者： Jiayu Yan Yiming Wan School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand Key Laboratory of Image Processing and Intelligent ControlMinistry of Education

As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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Non-Linear behavior of CNN model interpretation using Saliency Map

Non-Linear behavior of CNN model interpretation using Salien...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Sudhanshu Saurabh P.K. Gupta Department of Computer Science Engineering Jaypee University of Informationn Technology Solan H.P. India

ISBN: (纸本)9781665454025

The complex behavior of the network limits human perception of the network’s success, which is among the major challenges of the convolutional neural network. The saliency map is an useful approach for analyzing and visualizing the non -linear behavior of a feed forward network. Since the DNN model is nonlinear, the interpretation is not straightforward. We intro-duce an interpretation method that observes the saliency map and visual input, which is used to calculate the component-wise product to remove the noise. Using gradient-based visualization, multimodal convolutional networks perform for visual inputs. The component-wise product is used to visualize the input by analyzing the learned weights of the layers. The saliency map is generated by the guided back-propagation of the ReLU activation function. In this paper, we aim at a popular branch of description techniques, often referred to as saliency methods. Our main objective is to find input features such as image pixels.

关键词： Neuroimaging Visualization Gradient methods image analysis Computer architecture Functional magnetic resonance imaging Grid computing

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Text Embedding Bank for Detailed image Paragraph Captioning 35

Text Embedding Bank for Detailed Image Paragraph Captioning

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35th AAAI conference on Artificial Intelligence / 33rd conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Gupta, Arjun Shen, Zengming Huang, Thomas Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781577358664

Existing deep learning-based models for image captioning typically consist of an image encoder to extract visual features and a language model decoder, an architecture that has shown promising results in single high-level sentence generation. However, only the word-level guiding signal is available when the image encoder is optimized to extract visual features. The inconsistency between the parallel extraction of visual features and sequential text supervision limits its success when the length of the generated text is long (more than 50 words). We propose a new module, called the Text Embedding Bank (TEB), to address this problem for image paragraph captioning. This module uses the paragraph vector model to learn fixed-length feature representations from a variable-length paragraph. We refer to the fixed-length feature as the TEB. This TEB module plays two roles to benefit paragraph captioning performance. First, it acts as a form of global and coherent deep supervision to regularize visual feature extraction in the image encoder. Second, it acts as a distributed memory to provide features of the whole paragraph to the language model, which alleviates the long-term dependency problem. Adding this module to two existing state-of-the-art methods achieves a new state-of-the-art result on the paragraph captioning Stanford Visual Genome dataset.

关键词： Embeddings

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jinhai Yang Hua Yang Institution of Image Communication and Network Engineering Shanghai Jiao Tong University China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

Few-shot learning aims for rapid adaptation with few samples. Recently, cross-granularity few-shot learning has emerged as a promising research area, where models observe coarse labels but target fine-grained recognition among novel classes. As coarse supervision tends to eliminate feature discrimination among underlying sub-classes, existing methods commonly utilize self-supervision as a complement to explore intra-class variation. However, current methods suffer from an intrinsic conflict between contrastive learning and coarse supervision. In this paper, we locate the root cause of the intrinsic conflict. Then, we resolve it by exploiting the similarity among augmented views while ignoring the unreasonable constraint between negative pairs. Besides, we decouple contrastive learning and coarse supervision into parallel branches to better regularize the latent space. Albeit simple, our approach consistently outperforms state-of-the-art methods across different benchmarks.

关键词： Adaptation models Target recognition Semantics Signal processing Performance gain Benchmark testing Acoustics

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AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU 27

AMF-CSR: Adaptive Multi-Row Folding of CSR for SpMV on GPU

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27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Gao, Jianhua Ji, Weixing Liu, Jie Shao, Senhao Wang, Yizhuo Shi, Feng School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9781665408783

SpMV is a cost-dominant operation used in many iterative methods for solving large-scale sparse linear systems. However, irregular memory access of SpMV to the multiplied vector leads to low data locality and then harms the performance. This paper presents an adaptive multi-row folding of CSR (AMF-CSR) format for SpMV calculation on GPU. This new storage format supports the folding of the variable number of rows in order to achieve better load balancing in computation. AMF-CSR not only increases the density of non-zero elements in a folded row, thereby improving the access locality of the multiplied vector, but also merges an approximately equal number of nonzero elements in a folded row, hence achieving load balancing. The performance evaluation using 28 sparse matrices shows that the proposed SpMV algorithm based on AMF-CSR achieves the highest speedup of 4.11x and 3.62x on GTX 1080 Ti and Tesla V100 respectively against a fixed multi-row folding-based SpMV algorithm. Evaluation results using 450 regular sparse matrices and 450 irregular sparse matrices also show that AMF-CSR is superior to other SpMV implementations. © 2021 IEEE.

关键词： Graphics processing unit

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Exploring Algorithmic Design Choices for Low Latency CNN Deployment

Exploring Algorithmic Design Choices for Low Latency CNN Dep...

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International conference on High Performance Computing

作者： Changxin Li Sanmukh Kuppannagari Computer and Data Sciences Case Western Reserve University Cleveland USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Convolutional Neural Networks (CNN s) have demonstrated significant success in advancing image and video processing technologies, significantly outperforming traditional methods in both accuracy and efficiency. However, deploying CNN s effectively across diverse hardware platforms often faces the challenge of latency, which can critically impact real-time processing applications. In this work, we explore algorithmic design choices aimed at reducing latency in CNN deployments. We implement five convolution algorithms using SYCL and inte-grate them into three popular CNN models: VGG 16, Resnetl0l, and Inception V 4. By replacing the standard PyTorch Conv2d function with our SY CL- based implementations, we evaluate the execution time of each convolution layer and the overall model on G PU s. Our extensive experiments benchmark the performance of these algorithms against the baseline implementations of the PyTorch and Pytorch Extension for Intel. The results demonstrate significant improvements in execution time, underscoring the potential of these algorithmic choices for achieving low latency in CNN deployments.

关键词： Machine learning algorithms Convolution Graphics processing units parallel processing Prediction algorithms Hardware Data models Convolutional neural networks Low latency communication Standards

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Cortical Neural P Systems 15th

Cortical Neural P Systems

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15th International conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Fu, Zhongzheng Zhang, Luping Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

P systems are distributed and parallel computing models inspired from living cells. In this work, a variant of spiking neural P systems, cortical neural P systems, are proposed in the frame work of P systems combining the topology structure of basic P system and information processing style in spiking neurons. The computational power of cortical neural P systems is investigated. It is proved that cortical neural P systems are equivalent to Turing machines as number generators. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Turing machines

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Vision Control Unit in Fully Self Driving Vehicles using Xilinx MPSoC and Opensource Stack 21

Vision Control Unit in Fully Self Driving Vehicles using Xil...

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26th Asia and South Pacific Design Automation conference (ASP-DAC)

作者： Chakaravarthy, Ravikumar, V Kwon, Hyun Jiang, Hua Xilinx Inc San Jose CA 95124 USA

ISBN: (纸本)9781450379991

Fully self-driving (FSD) vehicles are becoming increasing popular over the last few years and companies are investing significantly into its research and development. In the recent years, FSD technology innovators like Tesla, Google etc. have been working on proprietary autonomous driving stacks and have been able to successfully bring the vehicle to the roads. On the other end, organizations like Autoware Foundation and Baidu are fueling the growth of self-driving mobility using open source stacks. These organizations firmly believe in enabling autonomous driving technology for everyone and support developing software stacks through the open source community that is SoC vendor agnostic. In this proposed solution we describe a vision control unit for a fully self-driving vehicle developed on Xilinx MPSoC platform using open source software components. The vision control unit of an FSD vehicle is responsible for camera video capture, image processing and rendering, AI algorithm processing, data and meta-data transfer to next stage of the FSD pipeline. In this proposed solution we have used many open source stacks and frameworks for video and AI processing. The processing of the video pipeline and algorithms take full advantage of the pipelining and parallelism using all the heterogenous cores of the Xilinx MPSoC. In addition, we have developed an extensible, scalable, adaptable and configurable AI backend framework, XTA, for acceleration purposes that is derived from a popular, open source AI backend framework, TVM-VTA. XTA uses all the MPSoC cores for its computation in a parallel and pipelined fashion. XTA also adapts to the compute and memory parameters of the system and can scale to achieve optimal performance for any given AI problem. The FSD system design is based on a distributed system architecture and uses open source components like Autoware for autonomous driving algorithms, ROS and distributed Data Services as a messaging middleware between the functional

关键词： FSD MPSoC AI XTA Autoware Heterogenous processing Vision Pipeline ROS

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13th International conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

13th International Conference on Innovations in Bio-Inspired...

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13th International conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

ISBN: (纸本)9783031274985

The proceedings contain 86 papers. The special focus in this conference is on Innovations in Bio-Inspired Computing and Applications. The topics include: A Novel Approach to the Two-Dimensional Cargo Load Problem;vehicle Detection from Aerial imagery Using Principal Component Analysis and Deep Learning;bio-inspired Heterogeneity in Swarm Robots;software Defect Prediction Using Cellular Automata as an Ensemble Strategy to Combine Classification Techniques;a Systematic Literature Review on Home Health Care Management;the Impact of the Size of the Partition in the Performance of Bat Algorithm;automatic Diagnosis Framework for Catheters and Tubes Semantic Segmentation and Placement Errors Detection;how Artificial Intelligence Can Revolutionize Software Testing Techniques;e-Assessment in Medical Education: From Paper to Platform;Evolution of Configuration Data in CGP Format Using parallel GA on Embryonic Fabric;DeepPRS: A Deep Learning Integrated Pattern Recognition Methodology for Secure Data in Cloud Environment;Automated Depression Diagnosis in MDD (Major Depressive Disorder) Patients Using EEG Signal;an Effective Deep Learning Classification of Diabetes Based Eye Disease Grades: An Retinal Analysis Approach;extracting and Analyzing Terms with the Component ‘Green’ in the Bulgarian Language: A Big Data Approach;Apartments Waste Disposal Location Evaluation Using TOPSIS and Fuzzy TOPSIS methods;detection of Cracks in Building Facades Using Infrared Thermography;optimizing Pre-processing for Foetal Cardiac Ultra Sound image Classification;a Review on Dimensionality Reduction for Machine Learning;detecting Depression on Social Platforms Using Machine Learning;impact of Green Hydrogen Production on Energy Pricing;Cross Synergetic Mobilenet-VGG16 for UML Multiclass Diagrams Classification;the Future in Fishfarms: An Ocean of Technologies to Explore;breast Cancer Identification Using Improved DarkNet53 Model;anomaly Detection Framework.

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