Artykuły w czasopismach: „Data-efficient Deep Learning”

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Chaudhary, Dr Sumit, Ms Neha Singh i Salaiya Pankaj. "Time-Efficient Algorithm for Data Annotation using Deep Learning". Indian Journal of Artificial Intelligence and Neural Networking 2, nr 5 (30.08.2022): 8–11. http://dx.doi.org/10.54105/ijainn.e1058.082522.

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Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is the fastest and its accuracy is very high compared to other classification algorithms. After classified the unlabeled data we use it as input and annotate data using deep learning techniques. In deep learning we use an auto annotator for annotating data. After annotating data, check the accuracy of annotated data and time efficiency of data annotation. In case the accuracy is low then we can retrain the data and make it more accurate.

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Biswas, Surojit, Grigory Khimulya, Ethan C. Alley, Kevin M. Esvelt i George M. Church. "Low-N protein engineering with data-efficient deep learning". Nature Methods 18, nr 4 (kwiecień 2021): 389–96. http://dx.doi.org/10.1038/s41592-021-01100-y.

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Edstrom, Jonathon, Yifu Gong, Dongliang Chen, Jinhui Wang i Na Gong. "Data-Driven Intelligent Efficient Synaptic Storage for Deep Learning". IEEE Transactions on Circuits and Systems II: Express Briefs 64, nr 12 (grudzień 2017): 1412–16. http://dx.doi.org/10.1109/tcsii.2017.2767900.

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Feng, Wenhui, Chongzhao Han, Feng Lian i Xia Liu. "A Data-Efficient Training Method for Deep Reinforcement Learning". Electronics 11, nr 24 (16.12.2022): 4205. http://dx.doi.org/10.3390/electronics11244205.

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Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state is set close to the goal so the agent can obtain an informative reward easily. As the training continues, the initial state distribution is set farther from the goal for the agent to explore more state space. Thus, the agent can obtain a reasonable policy through fewer interactions with the environment. To bridge the sim-to-real gap, the parameters for the output layer of the neural network for the value function are fine-tuned. An experiment on UAV maneuver control is conducted in the proposed training framework to verify the method. We demonstrate that data efficiency is different for the same data in different training stages.

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Hu, Wenjin, Feng Liu i Jiebo Peng. "An Efficient Data Classification Decision Based on Multimodel Deep Learning". Computational Intelligence and Neuroscience 2022 (4.05.2022): 1–10. http://dx.doi.org/10.1155/2022/7636705.

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A single model is often used to classify text data, but the generalization effect of a single model on text data sets is poor. To improve the model classification accuracy, a method is proposed that is based on a deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) and integrates multiple models trained by a deep learning network architecture to obtain a strong text classifier. Additionally, to increase the flexibility and accuracy of the model, various optimizer algorithms are used to train data sets. Moreover, to reduce the interference in the classification results caused by stop words in the text data, data preprocessing and text feature vector representation are used before training the model to improve its classification accuracy. The final experimental results show that the proposed model fusion method can achieve not only improved classification accuracy but also good classification effects on a variety of data sets.

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Mairittha, Nattaya, Tittaya Mairittha i Sozo Inoue. "On-Device Deep Learning Inference for Efficient Activity Data Collection". Sensors 19, nr 15 (5.08.2019): 3434. http://dx.doi.org/10.3390/s19153434.

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Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition.

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Duan, Yanjie, Yisheng Lv, Yu-Liang Liu i Fei-Yue Wang. "An efficient realization of deep learning for traffic data imputation". Transportation Research Part C: Emerging Technologies 72 (listopad 2016): 168–81. http://dx.doi.org/10.1016/j.trc.2016.09.015.

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Sashank, Madipally Sai Krishna, Vijay Souri Maddila, Vikas Boddu i Y. Radhika. "Efficient deep learning based data augmentation techniques for enhanced learning on inadequate medical imaging data". ACTA IMEKO 11, nr 1 (31.03.2022): 6. http://dx.doi.org/10.21014/acta_imeko.v11i1.1226.

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<p class="Abstract">The world has come to a standstill with the Coronavirus taking over. In these dire times, there are fewer doctors and more patients and hence, treatment is becoming more and more difficult and expensive. In recent times, Computer Science, Machine Intelligence, measurement technology has made a lot of progress in the field of Medical Science hence aiding the automation of a lot of medical activities. One area of progress in this regard is the automation of the process of detection of respiratory diseases (such as COVID-19). There have been many Convolutional Neural Network (CNN) architectures and approaches that have been proposed for Chest X-Ray Classification. But a big problem still remains and that is the minimal availability of Medical X-Ray Images due to improper measurements. Due to this minimal availability of Chest X-Ray data, most CNN classifiers do not get trained to an optimal level and the required standards for automating the process are not met. In order to overcome this problem, we propose a new deep learning based approach for accurate measurements of physiological data.</p>

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Petrovic, Nenad, i Djordje Kocic. "Data-driven framework for energy-efficient smart cities". Serbian Journal of Electrical Engineering 17, nr 1 (2020): 41–63. http://dx.doi.org/10.2298/sjee2001041p.

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Energy management is one of the greatest challenges in smart cities. Moreover, the presence of autonomous vehicles makes this task even more complex. In this paper, we propose a data-driven smart grid framework which aims to make smart cities energy-efficient focusing on two aspects: energy trading and autonomous vehicle charging. The framework leverages deep learning, linear optimization, semantic technology, domain-specific modelling notation, simulation and elements of relay protection. The evaluation of deep learning module together with code generation time and energy distribution cost reduction performed within the simulation environment also presented in this paper are given. According to the results, the achieved energy distribution cost reduction varies and depends from case to case.

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Yue, Yang, Bingyi Kang, Zhongwen Xu, Gao Huang i Shuicheng Yan. "Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 9 (26.06.2023): 11069–77. http://dx.doi.org/10.1609/aaai.v37i9.26311.

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Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL. These methods usually rely on contrastive learning and data augmentation to train a transition model, which is different from how the model is used in RL---performing value-based planning. Accordingly, the learned representation by these visual methods may be good for recognition but not optimal for estimating state value and solving the decision problem. To address this issue, we propose a novel method, called value-consistent representation learning (VCR), to learn representations that are directly related to decision-making. More specifically, VCR trains a model to predict the future state (also referred to as the "imagined state'') based on the current one and a sequence of actions. Instead of aligning this imagined state with a real state returned by the environment, VCR applies a Q value head on both of the states and obtains two distributions of action values. Then a distance is computed and minimized to force the imagined state to produce a similar action value prediction as that by the real state. We develop two implementations of the above idea for the discrete and continuous action spaces respectively. We conduct experiments on Atari 100k and DeepMind Control Suite benchmarks to validate their effectiveness for improving sample efficiency. It has been demonstrated that our methods achieve new state-of-the-art performance for search-free RL algorithms.

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Shin, Hyunkyung, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh i Honguk Woo. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions". Applied Sciences 12, nr 14 (21.07.2022): 7317. http://dx.doi.org/10.3390/app12147317.

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The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by investigating several sample-efficient deep learning techniques. We concentrate on applying these techniques to skin burn image analysis and classification. We first build a large-scale, professionally annotated dataset of skin burn images, which enables the establishment of convolutional neural network (CNN) models for burn severity assessment with high accuracy. We then deliberately set data limitation conditions and adapt several sample-efficient techniques, such as transferable learning (TL), self-supervised learning (SSL), federated learning (FL), and generative adversarial network (GAN)-based data augmentation, to those conditions. Through comprehensive experimentation, we evaluate the sample-efficient deep learning techniques for burn severity assessment, and show, in particular, that SSL models learned on a small task-specific dataset can achieve comparable accuracy to a baseline model learned on a six-times larger dataset. We also demonstrate the applicability of FL and GANs to model training under different data limitation conditions that commonly occur in the area of healthcare and medicine where deep learning models are adopted.

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Lyu, Daoming, Fangkai Yang, Bo Liu i Steven Gustafson. "SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 2970–77. http://dx.doi.org/10.1609/aaai.v33i01.33012970.

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Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner – controller – meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

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da Silva Lourenço, Catarina, Marleen C. Tjepkema-Cloostermans i Michel J. A. M. van Putten. "Efficient use of clinical EEG data for deep learning in epilepsy". Clinical Neurophysiology 132, nr 6 (czerwiec 2021): 1234–40. http://dx.doi.org/10.1016/j.clinph.2021.01.035.

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Cuayáhuitl, Heriberto. "A data-efficient deep learning approach for deployable multimodal social robots". Neurocomputing 396 (lipiec 2020): 587–98. http://dx.doi.org/10.1016/j.neucom.2018.09.104.

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Zhao, Junhui, Yiwen Nie, Shanjin Ni i Xiaoke Sun. "Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning". IEEE Access 8 (2020): 46713–22. http://dx.doi.org/10.1109/access.2020.2978530.

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Tovar, Nathaniel, Sean (Seok-Chul) Kwon i Jinseong Jeong. "Image Upscaling with Deep Machine Learning for Energy-Efficient Data Communications". Electronics 12, nr 3 (30.01.2023): 689. http://dx.doi.org/10.3390/electronics12030689.

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Advanced algorithms of image quality enhancement have been attracting substantial attention recently due to the successful business model of video streaming services. The extremely high image quality in video streaming demands a significant increase in the transmit data rate. In turn, the required ultrahigh data rate causes the saturation of the video streaming service network if there is no remedy for this situation. Compression algorithms have contributed to the energy-efficient transmission of data; however, they have almost reached the upper bound. The demand for ultrahigh image quality by the user is significantly increasing. Meanwhile, minimizing data transmission is inevitable in energy-efficient communications. Therefore, to improve energy efficiency, we propose to decrease the image resolution at the transmitter (Tx) and upscale the image at the receiver (Rx). However, standard upscaling does not yield ultrahigh-quality images. Deep machine learning contributes to image super-resolution techniques with the cost of enormous time and resources at the user end. Hence, it is inappropriate for real-time applications. With this motivation, this paper proposes a deep machine learning-based real-time image super-resolution with a residual neural network on the prevalent resources at the user end. The proposed scheme provides better quality than conventional image upscaling such as interpolation. The comprehensive simulation verifies that our scheme substantially outperforms the conventional methods, utilizing the seven-layer residual neural network.

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Li, Mengkun, i Yongjian Wang. "An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data". Wireless Communications and Mobile Computing 2020 (16.12.2020): 1–11. http://dx.doi.org/10.1155/2020/6661022.

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Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.

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Yuan, Mu, Lan Zhang, Xiang-Yang Li, Lin-Zhuo Yang i Hui Xiong. "Adaptive Model Scheduling for Resource-efficient Data Labeling". ACM Transactions on Knowledge Discovery from Data 16, nr 4 (31.08.2022): 1–22. http://dx.doi.org/10.1145/3494559.

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Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately, a single machine-learning model is not powerful enough to extract various semantic information from data. Given certain applications, such as image retrieval platforms and photo album management apps, it is often required to execute a collection of models to obtain sufficient labels. With limited computing resources and stringent delay, given a data stream and a collection of applicable resource-hungry deep-learning models, we design a novel approach to adaptively schedule a subset of these models to execute on each data item, aiming to maximize the value of the model output (e.g., the number of high-confidence labels). Achieving this lofty goal is nontrivial since a model’s output on any data item is content-dependent and unknown until we execute it. To tackle this, we propose an Adaptive Model Scheduling framework, consisting of (1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and (2) two heuristic algorithms to adaptively schedule the model execution order under a deadline or deadline-memory constraints, respectively. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on five diverse image datasets and 30 popular image labeling models to demonstrate the effectiveness of our design: our design could save around 53% execution time without loss of any valuable labels.

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Onofrey, John A., Lawrence H. Staib, Xiaojie Huang, Fan Zhang, Xenophon Papademetris, Dimitris Metaxas, Daniel Rueckert i James S. Duncan. "Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation". Annual Review of Biomedical Engineering 22, nr 1 (4.06.2020): 127–53. http://dx.doi.org/10.1146/annurev-bioeng-060418-052147.

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Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

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Bhat, Sanjit, David Lu, Albert Kwon i Srinivas Devadas. "Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning". Proceedings on Privacy Enhancing Technologies 2019, nr 4 (1.10.2019): 292–310. http://dx.doi.org/10.2478/popets-2019-0070.

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Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

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Wang, Yang, Yutong Li, Ting Wang i Gang Liu. "Towards an energy-efficient Data Center Network based on deep reinforcement learning". Computer Networks 210 (czerwiec 2022): 108939. http://dx.doi.org/10.1016/j.comnet.2022.108939.

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Shiloh, Lihi, Avishay Eyal i Raja Giryes. "Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach". Journal of Lightwave Technology 37, nr 18 (15.09.2019): 4755–62. http://dx.doi.org/10.1109/jlt.2019.2919713.

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Yi, Deliang, Xin Zhou, Yonggang Wen i Rui Tan. "Efficient Compute-Intensive Job Allocation in Data Centers via Deep Reinforcement Learning". IEEE Transactions on Parallel and Distributed Systems 31, nr 6 (1.06.2020): 1474–85. http://dx.doi.org/10.1109/tpds.2020.2968427.

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Jeong, Seunghwan, Gwangpyo Yoo, Minjong Yoo, Ikjun Yeom i Honguk Woo. "Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning". Sensors 19, nr 20 (11.10.2019): 4410. http://dx.doi.org/10.3390/s19204410.

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Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

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He, Yan, Bin Fu, Jian Yu, Renfa Li i Rucheng Jiang. "Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks". Applied Sciences 10, nr 24 (15.12.2020): 8934. http://dx.doi.org/10.3390/app10248934.

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Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

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Wu, Chunyi, i Ya Li. "FLOM: Toward Efficient Task Processing in Big Data with Federated Learning". Security and Communication Networks 2022 (27.01.2022): 1–16. http://dx.doi.org/10.1155/2022/5277362.

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With the diversification and individuation of user requirements as well as the rapid development of computing technology, the large-scale tasks processing for big data in edge computing environment has become a research focus nowadays. Many recent efforts for task processing are designed and implemented based on some traditional protocols and optimization methods. Therefore, it is more difficult to explore the task allocation strategy that maximizes the overall system revenue from the perspective of global load balancing. In order to overcome this problem, a large-scale tasks processing approach called Federated Learning based Optimization Methodology (FLOM) for large-scale tasks processing was presented to achieve accurate task classification and overall load balancing while satisfying task allocation requirements. FLOM performs the data aggregation and establishes the personalized models by federated learning. The deep network model is designed for deep feature learning of task requests and hosts in the substrate network. The experimental results show the capability of FLOM in terms of large-scale task classification as well as allocation.

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Zhang, Lan, Yu Feng Nie i Zhen Hai Wang. "Image De-Noising Using Deep Learning". Applied Mechanics and Materials 641-642 (wrzesień 2014): 1287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.1287.

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Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

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Salakhutdinov, Ruslan, i Geoffrey Hinton. "An Efficient Learning Procedure for Deep Boltzmann Machines". Neural Computation 24, nr 8 (sierpień 2012): 1967–2006. http://dx.doi.org/10.1162/neco_a_00311.

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We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.

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Ren, Jing, Xishi Huang i Raymond N. Huang. "Efficient Deep Reinforcement Learning for Optimal Path Planning". Electronics 11, nr 21 (7.11.2022): 3628. http://dx.doi.org/10.3390/electronics11213628.

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In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal path planning for mobile robots using dynamic programming (DP)-based data collection. The proposed method can overcome the slow learning process and improve training data quality inherently in DRL algorithms. The main idea of our approach is as follows. First, we mapped the dynamic programming method to typical optimal path planning problems for mobile robots, and created a new efficient DP-based method to find an exact, analytical, optimal solution for the path planning problem. Then, we used high-quality training data gathered using the DP method for DRL, which greatly improves training data quality and learning efficiency. Next, we established a two-stage reinforcement learning method where, prior to the DRL, we employed extreme learning machines (ELM) to initialize the parameters of actor and critic neural networks to a near-optimal solution in order to significantly improve the learning performance. Finally, we illustrated our method using some typical path planning tasks. The experimental results show that our DRL method can converge much easier and faster than other methods. The resulting action neural network is able to successfully guide robots from any start position in the environment to the goal position while following the optimal path and avoiding collision with obstacles.

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Blank, Andreas, Lukas Baier, Oguz Kedilioglu, Xuebei Zhu, Maximilian Metzner i Jörg Franke. "Effiziente KI-Adaption durch synthetische Daten/Efficient AI Adaption using Synthetic Data". wt Werkstattstechnik online 111, nr 10 (2021): 759–62. http://dx.doi.org/10.37544/1436-4980-2021-10-105.

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Die Produktion ist geprägt durch einen Antagonismus aus Flexibilität und Produktivität. Objektmanipulation gestützt durch Deep-Learning-basierte, autonome Roboterfähigkeiten bietet Potenzial, bestehende Herausforderungen zu lösen. Der Aufwand zur Erzeugung zweckmäßiger Daten ist allerdings hoch. Im Beitrag vorgestellt und bewertet wird eine Methode zur zeiteffizienten Datengenerierung für die Objekterkennung mittels synthetischer Daten.   Production is characterized by an antagonism between flexibility and productivity. Deep Learning-based autonomous robot skills for object manipulation offer potential to solve existing challenges. Currently, the effort to generate appropriate datasets to adapt new components is time-consuming. In this research context, we present and evaluate a method for time-efficient data generation for object recognition based on synthetic data.

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Hao, Ruqian, Lin Liu, Jing Zhang, Xiangzhou Wang, Juanxiu Liu, Xiaohui Du, Wen He, Jicheng Liao, Lu Liu i Yuanying Mao. "A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning". Journal of Healthcare Engineering 2022 (27.02.2022): 1–11. http://dx.doi.org/10.1155/2022/1929371.

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Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and reliable solution for an automatic early diagnosis of vaginitis. However, deep neural networks require massive well-annotated data. Manual annotation of microscopic images is highly cost extensive because it not only is a time-consuming process but also needs highly trained people (doctors, pathologists, or technicians). Most existing active learning approaches are not applicable in microscopic images due to the nature of complex backgrounds and numerous formed elements. To address the problem of high cost of labeling microscopic images, we present a data-efficient framework for the identification of vaginitis based on transfer learning and active learning strategies. The proposed informative sample selection strategy selected the minimal training subset, and then the pretrained convolutional neural network (CNN) was fine-tuned on the selected subset. The experiment results show that the proposed pipeline can save 37.5% annotation cost while maintaining competitive performance. The proposed promising novel framework can significantly save the annotation cost and has the potential of extending widely to other microscopic imaging applications, such as blood microscopic image analysis.

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Eide, Siri S., Michael A. Riegler, Hugo L. Hammer i John Bjørnar Bremnes. "Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources". Sensors 22, nr 7 (6.04.2022): 2802. http://dx.doi.org/10.3390/s22072802.

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Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.

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Shin, Tae-Ho, i Soo-Hyung Kim. "Utility Analysis about Log Data Anomaly Detection Based on Federated Learning". Applied Sciences 13, nr 7 (1.04.2023): 4495. http://dx.doi.org/10.3390/app13074495.

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Logs that record system information are managed in anomaly detection, and more efficient anomaly detection methods have been proposed due to their increase in complexity and scale. Accordingly, deep learning models that automatically detect system anomalies through log data learning have been proposed. However, in existing log anomaly detection models, user logs are collected from the central server system, exposing the data collection process to the risk of leaking sensitive information. A distributed learning method, federated learning, is a trend proposed for artificial intelligence learning regarding sensitive information because it guarantees the anonymity of the collected user data and collects only weights learned from each local server in the central server. In this paper, we executed an experiment regarding system log anomaly detection using federated learning. The results demonstrate the feasibility of applying federated learning in deep-learning-based system-log anomaly detection compared to the existing centralized learning method. Moreover, we present an efficient deep-learning model based on federated learning for system log anomaly detection.

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Moss, Adam. "Accelerated Bayesian inference using deep learning". Monthly Notices of the Royal Astronomical Society 496, nr 1 (28.05.2020): 328–38. http://dx.doi.org/10.1093/mnras/staa1469.

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ABSTRACT We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in ∼20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from https://github.com/adammoss/nnest.

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Vengateshwaran, Mr M. "Efficient Deep Learning Approach for Dimensionality Reduction using Micro blogs from Big data". International Journal for Research in Applied Science and Engineering Technology V, nr III (9.03.2017): 5–10. http://dx.doi.org/10.22214/ijraset.2017.3002.

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Khodaparast, Seyed Saeed, Xiao Lu, Ping Wang i Uyen Trang Nguyen. "Deep Reinforcement Learning Based Energy Efficient Multi-UAV Data Collection for IoT Networks". IEEE Open Journal of Vehicular Technology 2 (2021): 249–60. http://dx.doi.org/10.1109/ojvt.2021.3085421.

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Jiang, Rong, Zhipeng Wang, Bin He, Yanmin Zhou, Gang Li i Zhongpan Zhu. "A data-efficient goal-directed deep reinforcement learning method for robot visuomotor skill". Neurocomputing 462 (październik 2021): 389–401. http://dx.doi.org/10.1016/j.neucom.2021.08.023.

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NAMOZOV, A., i Y. I. CHO. "An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data". Advances in Electrical and Computer Engineering 18, nr 4 (2018): 121–28. http://dx.doi.org/10.4316/aece.2018.04015.

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Hassan, Mohammad Mehedi, Abdu Gumaei, Ahmed Alsanad, Majed Alrubaian i Giancarlo Fortino. "A hybrid deep learning model for efficient intrusion detection in big data environment". Information Sciences 513 (marzec 2020): 386–96. http://dx.doi.org/10.1016/j.ins.2019.10.069.

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Eshghi, Mohammad, Alireza Souri, Babak Majidi i Amin Fadaeddini. "Data augmentation using fast converging CIELAB-GAN for efficient deep learning dataset generation". International Journal of Computational Science and Engineering 26, nr 4 (2023): 459–69. http://dx.doi.org/10.1504/ijcse.2023.10057257.

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Fadaeddini, Amin, Babak Majidi, Alireza Souri i Mohammad Eshghi. "Data augmentation using fast converging CIELAB-GAN for efficient deep learning dataset generation". International Journal of Computational Science and Engineering 26, nr 4 (2023): 459–69. http://dx.doi.org/10.1504/ijcse.2023.132152.

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Wei, Shengyun, Zhaolong Sun, Zhenyi Wang, Feifan Liao, Zhen Li i Haibo Mi. "An Efficient Data Augmentation Method for Automatic Modulation Recognition from Low-Data Imbalanced-Class Regime". Applied Sciences 13, nr 5 (1.03.2023): 3177. http://dx.doi.org/10.3390/app13053177.

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The application of deep neural networks to address automatic modulation recognition (AMR) challenges has gained increasing popularity. Despite the outstanding capability of deep learning in automatic feature extraction, predictions based on low-data regimes with imbalanced classes of modulation signals generally result in low accuracy due to an insufficient number of training examples, which hinders the wide adoption of deep learning methods in practical applications of AMR. The identification of the minority class of samples can be crucial, as they tend to be of higher value. However, in AMR tasks, there is a lack of attention and effective solutions to the problem of Imbalanced-class in a low-data regime. In this work, we present a practical automatic data augmentation method for radio signals, called SigAugment, which incorporates eight individual transformations and effectively improves the performance of AMR tasks without additional searches. It surpasses existing data augmentation methods and mainstream methods for solving low-data and imbalanced-class problems on multiple datasets. By simply embedding SigAugment into the training pipeline of an existing model, it can achieve state-of-the-art performance on benchmark datasets and dramatically improve the classification accuracy of minority classes in the low-data imbalanced-class regime. SigAugment can be trained for uniform use on different types of models and datasets and works right out of the box.

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Xu, Wencai. "Efficient Distributed Image Recognition Algorithm of Deep Learning Framework TensorFlow". Journal of Physics: Conference Series 2066, nr 1 (1.11.2021): 012070. http://dx.doi.org/10.1088/1742-6596/2066/1/012070.

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Abstract Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model.

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Eltehewy, Rokaya, Ahmed Abouelfarag i Sherine Nagy Saleh. "Efficient Classification of Imbalanced Natural Disasters Data Using Generative Adversarial Networks for Data Augmentation". ISPRS International Journal of Geo-Information 12, nr 6 (17.06.2023): 245. http://dx.doi.org/10.3390/ijgi12060245.

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Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social media platforms, extensive research has focused on damage assessment. Through the use of geospatial data related to such incidents, the visual characteristics of these images can quickly determine the safety situation in the region. However, training accurate disaster classification models has proven to be challenging due to the lack of labeled imagery data in this domain. This paper proposes a disaster classification framework, which combines a set of synthesized diverse disaster images generated using generative adversarial networks (GANs) and domain-specific fine-tuning of a deep convolutional neural network (CNN)-based model. The proposed model utilizes bootstrap aggregating (bagging) to further stabilize the target predictions. Since past work in this domain mainly suffers from limited data resources, a sample dataset that highlights the issue of imbalanced classification of multiple natural disasters was constructed and augmented. Qualitative and quantitative experiments show the validity of the data augmentation method employed in producing a balanced dataset. Further experiments with various evaluation metrics verified the proposed framework’s accuracy and generalization ability across different classes for the task of disaster classification in comparison to other state-of-the-art techniques. Furthermore, the framework outperforms the other models by an average validation accuracy of 11%. These results provide a deep learning solution for real-time disaster monitoring systems to mitigate the loss of lives and properties.

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Rix, Tom, Kris K. Dreher, Jan-Hinrich Nölke, Melanie Schellenberg, Minu D. Tizabi, Alexander Seitel i Lena Maier-Hein. "Efficient Photoacoustic Image Synthesis with Deep Learning". Sensors 23, nr 16 (10.08.2023): 7085. http://dx.doi.org/10.3390/s23167085.

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Photoacoustic imaging potentially allows for the real-time visualization of functional human tissue parameters such as oxygenation but is subject to a challenging underlying quantification problem. While in silico studies have revealed the great potential of deep learning (DL) methodology in solving this problem, the inherent lack of an efficient gold standard method for model training and validation remains a grand challenge. This work investigates whether DL can be leveraged to accurately and efficiently simulate photon propagation in biological tissue, enabling photoacoustic image synthesis. Our approach is based on estimating the initial pressure distribution of the photoacoustic waves from the underlying optical properties using a back-propagatable neural network trained on synthetic data. In proof-of-concept studies, we validated the performance of two complementary neural network architectures, namely a conventional U-Net-like model and a Fourier Neural Operator (FNO) network. Our in silico validation on multispectral human forearm images shows that DL methods can speed up image generation by a factor of 100 when compared to Monte Carlo simulations with 5×108 photons. While the FNO is slightly more accurate than the U-Net, when compared to Monte Carlo simulations performed with a reduced number of photons (5×106), both neural network architectures achieve equivalent accuracy. In contrast to Monte Carlo simulations, the proposed DL models can be used as inherently differentiable surrogate models in the photoacoustic image synthesis pipeline, allowing for back-propagation of the synthesis error and gradient-based optimization over the entire pipeline. Due to their efficiency, they have the potential to enable large-scale training data generation that can expedite the clinical application of photoacoustic imaging.

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Ayad, Hayder, Ikhlas Watan Ghindawi i Mustafa Salam Kadhm. "Lung Segmentation Using Proposed Deep Learning Architecture". International Journal of Online and Biomedical Engineering (iJOE) 16, nr 15 (15.12.2020): 141. http://dx.doi.org/10.3991/ijoe.v16i15.17115.

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<div id="titleAndAbstract"><table class="data" width="100%"><tbody><tr valign="top"><td class="value">The Prediction and detection disease in human lungs are a very critical operation. It depends on an efficient view of the CT images to the doctors. It depends on an efficient view of the CT images to the doctors. The clear view of the images to clearly identify the disease depends on the segmentation that may save people lives. Therefore, an accurate lung segmentation system from CT image based on proposed CNN architecture is proposed. The system used weighted softmax function the improved the segmentation accuracy. By experiments, the system achieved a high segmentation accuracy 98.9% using LIDC-IDRI CT lung images database.</td></tr></tbody></table></div><div id="indexing"> </div>

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Kumar, Ravinder, i Lokesh Kumar Shrivastav. "Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis". Journal of Information Technology Research 15, nr 1 (styczeń 2022): 1–20. http://dx.doi.org/10.4018/jitr.2022010101.

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Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.

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Polanski, Jaroslaw. "Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry". International Journal of Molecular Sciences 23, nr 5 (3.03.2022): 2797. http://dx.doi.org/10.3390/ijms23052797.

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The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.

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Istiaque, Shah Md, Asif Iqbal Khan i Sajjad Waheed. "Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning". European Journal of Engineering Research and Science 5, nr 10 (8.10.2020): 1168–73. http://dx.doi.org/10.24018/ejers.2020.5.10.2128.

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In the present world, digital intruders can exploit the vulnerabilities of a network and are capable to collapse even a country. Attack in Estonia by digital intruders, attack in Iran's nuclear plant and intrusion of spyware in smart phone depicts the efficiency of attackers. Furthermore, centralized firewall system is not enough for ensuring a secured network. Hence, in the age of big data, where availability of data is huge and computation capability of PC is also high, there machine learning and network security have become two inseparable issues. In this thesis, KDD Cup’99 intrusion detection dataset is used. Total 3, 11,030 numbers of records with 41 features are available in the dataset. For finding the anomalies of the network four machine learning methods are used like Classification and Regression Tree (CART), Random Forest, Naive Bayes and Multi-Layer Perception. Initially all 41 features are used to find out the accuracy. Among all the methods, Random Forest provides 98.547% accuracy in intrusion detection which is maximum, and CART shows maximum accuracy (99.086%) to find normal flow of data. Gradually selective 15 features were taken to test the accuracy and it was found that Random Forest is still efficient (accuracy 98.266%) in detecting the fault of the network. In both cases MLP found to be a stable method where accuracy regarding benign data and intrusion are always close to 95% (93.387%, 94.312% and 95.0075, 93.652% respectively). Finally, an IDS model is proposed where Random Forest of ML method and MLP of DL method is incorporated, to handle the intrusion in a most efficient manner.

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Istiaque, Shah Md, Asif Iqbal Khan i Sajjad Waheed. "Smart Intrusion Detection System Comprised of Machine Learning and Deep Learning". European Journal of Engineering and Technology Research 5, nr 10 (8.10.2020): 1168–73. http://dx.doi.org/10.24018/ejeng.2020.5.10.2128.

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In the present world, digital intruders can exploit the vulnerabilities of a network and are capable to collapse even a country. Attack in Estonia by digital intruders, attack in Iran's nuclear plant and intrusion of spyware in smart phone depicts the efficiency of attackers. Furthermore, centralized firewall system is not enough for ensuring a secured network. Hence, in the age of big data, where availability of data is huge and computation capability of PC is also high, there machine learning and network security have become two inseparable issues. In this thesis, KDD Cup’99 intrusion detection dataset is used. Total 3, 11,030 numbers of records with 41 features are available in the dataset. For finding the anomalies of the network four machine learning methods are used like Classification and Regression Tree (CART), Random Forest, Naive Bayes and Multi-Layer Perception. Initially all 41 features are used to find out the accuracy. Among all the methods, Random Forest provides 98.547% accuracy in intrusion detection which is maximum, and CART shows maximum accuracy (99.086%) to find normal flow of data. Gradually selective 15 features were taken to test the accuracy and it was found that Random Forest is still efficient (accuracy 98.266%) in detecting the fault of the network. In both cases MLP found to be a stable method where accuracy regarding benign data and intrusion are always close to 95% (93.387%, 94.312% and 95.0075, 93.652% respectively). Finally, an IDS model is proposed where Random Forest of ML method and MLP of DL method is incorporated, to handle the intrusion in a most efficient manner.

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