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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WIth PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th international conference on learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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DISENTANGLEMENT OF CORRELATED FACTORS VIA HAUSDORFF FACTORIZED SUPPORT 11

DISENTANGLEMENT OF CORRELATED FACTORS VIA HAUSDORFF FACTORIZ...

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11th international conference on learning Representations, ICLR 2023

作者： Roth, Karsten Ibrahim, Mark Akata, Zeynep Vincent, Pascal Bouchacourt, Diane University of Tübingen ELLIS Germany Meta AI FAIR United States MPI for Intelligent Systems Germany MILA Université de Montréal DIRO CIFAR Canada

A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a model's representation with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumption: that factors are statistically independent. In reality, factors (like object color and shape) are correlated. To address this limitation, we consider the use of a relaxed disentanglement criterion - the Hausdorff Factorized Support (HFS) criterion - that encourages only pairwise factorized support, rather than a factorial distribution, by minimizing a Hausdorff distance. this allows for arbitrary distributions of the factors over their support, including correlations between them. We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over +60% in relative improvement over existing disentanglement methods. In addition, we find that leveraging HFS for representation learning can even facilitate transfer to downstream tasks such as classification under distribution shifts. We hope our original approach and positive empirical results inspire further progress on the open problem of robust generalization. Code available at https://***/facebookresearch/disentangling-correlated-factors. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

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Research on Sea Surface Target Detection Algorithm Based on Deep learning 11

Research on Sea Surface Target Detection Algorithm Based on ...

引用

11th IEEE international conference on Computer Science and Network Technology, ICCSNT 2023

作者： Dong, Kaiyuan Liu, Tao Du, Hongwang Zheng, Yan College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China China Faw Group Corporation Beijing China

ISBN: (纸本)9798350311594

the ocean plays an increasingly prominent role in safeguarding national sovereignty, security, and development interests. the breakthrough and application of key technologies such as unmanned surface vessel intelligence is an important technical support for understanding the ocean, utilizing the ocean, and managing the ocean. the detection of sea surface objects based on visual images is an important way to realize the intelligent environment perception of unmanned surface vessels. Aiming at the difficulties in feature extraction and low detection accuracy of traditional sea surface target detection algorithms, this paper proposes an unmanned surface vessel sea surface target detection algorithm based on deep learning. We propose Cross-layer weighted Path Aggregation Network, by introducing two bottom-up paths for weighted feature fusion, to obtain feature interaction across dimensions and spaces, and propose Double residual group convolution to apply to backbone network and Cross-layer weighted Path Aggregation Between networks, the feature layer fuses rich semantic information and fine-grained information. the ECA attention mechanism is introduced to bring large performance gains at the cost of fewer parameters. Experiments show that the AP of our model can reach 74.9%, and when deployed on an unmanned surface vessel, it can detect occluded targets and long-distance small targets in real time under complex sea conditions. It has great potential in improving the marine survey capability of unmanned surface vessel and collecting marine data. © 2023 IEEE.

关键词： Feature extraction

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Graph Convolutional Network-Based Federated learning Method for Distribution System Topology Identification 11

Graph Convolutional Network-Based Federated Learning Method ...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Wu, Huayi Xu, Zhao Ding, Yibo Shi, Wenzhuo Zhang, Aoxiang Liu, Yugui The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen Research Institute The Hong Kong Polytechnic University Shenzhen China Research Institute of Smart Energy The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798350368345

Distribution system topology identification is the basic function for system operation but is hindered by data privacy concerns and uncertainties arising from high penetration of renewable energy sources (RES). To tackle these issues, a graph convolutional network-based federated learning method (GraphFed) is proposed for topology identification while ensuring data privacy. Specifically, to remove the data leakage risk, the federated learning (FL) algorihm is proposed for topology identification in a decentralized learning framework. Besides, the proposed graph convolutional network (GCN) leverages a novel node embedding-based graph shift operator to automatically represent the graphical features of the power system data to achieve enhanced topology identification accuracy. Comparative experiments are conducted on the IEEE 33-node distribution system, demonstrating the effectiveness of the proposed GraphFed method. © 2024 IEEE.

关键词： Federated learning

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A Transfer learning-based Model for Individualized Clustered Seizure Prediction Using Intracranial EEG 11

A Transfer Learning-based Model for Individualized Clustered...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Cao, Yurui Saboo, Krishnakant V. Kremen, Vaclav Sladky, Vladimir Gregg, Nicholas M. Arnold, Paul M. Pappu, Suguna Karoly, Philippa J. Freestone, Dean R. Cook, Mark J. Worrell, Gregory A. Iyer, Ravishankar K. Univ Illinois Urbana IL 61801 USA Mayo Clin Rochester MN USA Carle Illinois Coll Med Urbana IL USA Univ Melbourne Melbourne Vic Australia Seer Med Pty Ltd Melbourne Vic Australia

ISBN: (纸本)9781665462921

Clustered seizures are prevalent among people with epilepsy and can increase mortality risk. While past research has mainly focused on seizure cluster detection, a few recent studies predict seizure clustering by determining whether there will be more seizures in the next 24 hours after the termination of a seizure. Moreover, personalized prediction of clustered seizures in the presence of limited and imbalanced data remains an outstanding problem. We address this problem using a novel transfer learning model to predict seizure clustering within a 24-hour window. To compensate for the limited and imbalanced available data, for each target patient, the model combines trained individual-level predictive models of the target patient and two other patients whose seizure patterns are similar to those of the target patient. Approximate Kullback-Leibler divergence is used to measure the similarity between patients in high-dimensional data. the proposed model is evaluated on a long-term ambulatory intracranial EEG dataset. Compared with individualized predictive models, the proposed model improves F1 scores for patients with limited or highly imbalanced data by up to 51.0%. In addition, the proposed model achieves an average F1 score of 0.702 and an area under the precision-recall curve of 0.809. Our model can be clinically helpful in guiding the treatment of clustered seizures.

关键词： Individualized prediction Intracranial EEG Clustered seizures Deep learning Transfer learning

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BENCHMARKING OFFLINE REINFORCEMENT learning ON REAL-ROBOT HARDWARE 11

BENCHMARKING OFFLINE REINFORCEMENT LEARNING ON REAL-ROBOT HA...

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11th international conference on learning Representations, ICLR 2023

作者： Gürtler, Nico Blaes, Sebastian Kolev, Pavel Widmaier, Felix Wüthrich, Manuel Bauer, Stefan Schölkopf, Bernhard Martius, Georg Max Planck Institute for Intelligent Systems Germany Harvard University United States KTH Stockholm Sweden

learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular remains an open problem in its general form. the combination of offline reinforcement learning with large diverse datasets, however, has the potential to lead to a breakthrough in this challenging domain analogously to the rapid progress made in supervised learning in recent years. To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation;ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging. We evaluate prominent open-sourced offline reinforcement learning algorithms on the datasets and provide a reproducible experimental setup for offline reinforcement learning on real systems. Visit https://***/view/benchmarking-offline-rl-real for more details. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Recognition of Intracranial Hemorrhage with its Subtypes from CT Images using Deep learning Approach 7

Recognition of Intracranial Hemorrhage with its Subtypes fro...

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7th international conference on intelligent Computing and Control Systems, ICICCS 2023

作者： Rashid, Md. Harun Or Ahmed, Boshir Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350397253

Intracranial Hemorrhage (ICH) or Intracranial Bleeding is a severe problem for a person who rushes towards death unless it is recognized accurately within a short period. Commonly, it occurs within the skull or brain. Since the brain is the most crucial part of the human body and consists of soft tissue, its lesion detection, and treatment are so sensitive and also challenging within a faster time. the radiologist analyzes brain CT images to diagnose diseases consuming more time that affects a severe patient negatively. Being the process slow, non-automated, and costly, an automated deep learning approach is proposed to diagnose the problems accurately within the shortest time. For our research, the Radiological Society of North America (RSNA) dataset is considered, which consists of 752,803 training images and 121,232 test images. Normalization, windowing, and augmentation are used as preprocessing techniques on the training data, and during the model training, 30% of data are treated as validation data for hyper-tuning. After the successful completion of learning, this study has applied the test data to evaluate the performance of the proposed model, and it was observed that the proposed model performs better in the recognition of ICH with 98% accuracy. © 2023 IEEE.

关键词： Deep learning

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Adversarial Debiasing techniques towards 'fair' skin lesion classification 11

Adversarial Debiasing techniques towards 'fair' skin lesion ...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Correa-Medero, Ramon L. Patel, Bhavik Banerjee, Imon Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Mayo Clin Arizona Dept Radiol Phoenix AZ USA

ISBN: (纸本)9781665462921

Early detection of skin cancer lesions impacts overall patient survival. However, as shown in the literature, people of color have worse prognoses and lower survival rates than people with lighter skin tones. Often, this is the result of delayed or incorrect diagnoses for people of color. Deep learning could provide an effective screening technology with readily available image-capturing techniques, even from a mobile phone. However, often skin complexion biases limit the accuracy of the deep learning models as the applications are mainly trained on data that is predominantly light skin. We propose an adversarial debiasing method with partial learning that produces fairer outcomes for both lighter and darker skin colors. the model unlearns the skin color bias by using an additional classifier to penalize the learning of features specific to skin color. In the partial learning, we added Testing with Concept Activation Vector(TCAV) to select the particular layer where the skin color features are most discernable. We evaluated the performance internally on Fizpatrick17k and externally on ISIC datasets. the debiased model performed equally well for identifying malignant cases on both light and darker skin color. In conclusion, our work finds the adversarial debiasing techniques able to increase the skin lesion model's performance for all the skin color variations without the need for a balanced training dataset and provide a generalization to the external datasets.

关键词： Deep learning Fairness Melanoma Adversarial Training

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DeepFake Video Detection Using Machine learning and Deep learning Techniques 11

DeepFake Video Detection Using Machine Learning and Deep Lea...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sarala, Lokireddy Sridevi, Chittiboina Chowdary, Rayapati Akash Gargeye, Mulam Hema Gnana Prasuna V.R. Siddhartha Engineering College Electronics and Communication Vijayawada India

ISBN: (纸本)9798350350357

the current issue facing the community is determining the legitimacy of online content, including movies and pictures generated by machine learning, in light of the development of Generative adversarial networks (GAN) and other deep learning-based DeepFake approaches. there is an extraordinary chance that we may severe violations of fundamental human rights combined with an inevitable, fundamental shift in the way people interact in society. Evidence of misinformation and manipulation of news headlines, medical (dis)information, and invasions of privacy have already been demonstrated. the objective of this proposed project is to efficiently identify DeepFake photos using an online image database. the categorization of real from false photos using convolutional neural networks and data from a sizable internet database is the main topic of this study. Our comparison of three distinct convolutional neural networks was our goal. 1) DenseNet, 2) VGG Face and 3) personalized CNN structure. Future research will examine whether real and false pictures cluster independently using unsupervised clustering techniques or auto-encoders. It would also involve using CNN visualization techniques to give our models more interpretability and transparency. © 2024 IEEE.

关键词： Generative adversarial networks

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FedLDC: Federated learning with Loss-Dependent Coefficients in Pedestrian Detection 11

FedLDC: Federated Learning with Loss-Dependent Coefficients ...

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11th IEEE international conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Akpinar, Emin Bolat, Bulent Taskiran, Murat Yildiz Technical University Dept. of Electronics and Communication Engineering Istanbul Turkey

ISBN: (纸本)9798350308433

One of the advantages of autonomous driving systems is that they prevent possible accidents and thus reduce the number of casualties and injuries. With the development of artificial intelligence, there are many studies in this field. Achieving a successful generalization performance in such applications is possible by training the model with various backgrounds and as much unique data as possible. However, the creation of such diverse and large datasets by a single community requires a huge labor and financial cost, and even if such datasets are created, the direct sharing of these datasets brings personal data privacy problems. Since federated learning eliminates the need for data sharing, it eliminates privacy issues and allows many datasets to be used together for training without the need to share different datasets with a server. Many federated learning methods such as FedAvg, FedProx, SCAFFOLD have been proposed in the literature. However, in applications where datasets of different difficulty and comprehensiveness are used, the global model weights generated are not sufficient to capture a good representation of the datasets used in the application. For this reason, we propose Federated learning with Loss-Dependent Coefficients (FedLDC), which uses the Faster R-CNN ResNet50 FPN network as local model and offers a new federated learning approach that assigns higher coefficients to datasets with high loss values but also with more diverse background and unique data by utilizing the loss value during training. In our study, we compared FedLDC with both FedAvg, the first example of federated learning, and the standard training methodology, which is not federated. As a result of the tests we performed on three different datasets, FedLDC achieved the most successful result with a Miss Rate (MR) of 0.13 on Caltech Pedestrian and 0.07 on CityPersons, while it achieved a MR of 0.10 on the ECP Day dataset, competitive with the standard training methodology, which achie

关键词： Large datasets

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