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检索条件"任意字段=Conference on Artificial Intelligence and Machine Learning in Defense Applications IV"

共 594 条记录，以下是121-130 订阅

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On the Distributional Convergence of Temporal Difference learning

On the Distributional Convergence of Temporal Difference Lea...

引用

5th International Workshop on learning with Imbalanced Domains - Theory and applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dai, Jie Chen, Xuguang Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9783031434204;9783031434211

Temporal Difference (TD) learning is one of the most simple but efficient algorithms for policy evaluation in reinforcement learning. Although the finite-time convergence results of the TD algorithm are abundant now, the distributional convergence has still been blank. This paper shows that TD with constant step size simulates Markov chains converging to some stationary distribution under both i.i.d. and Markov chain observation models. We prove that TD enjoys the geometric distributional convergence rate and show how the step size affects the expectation and covariance of the stationary distribution. All assumptions used in our paper are mild and common in the TD community. Our proved results indicate a tradeoff between the convergence speed and accuracy for TD. Based on our theoretical findings, we explain why the Jacobi preconditioner can accelerate the TD algorithms.

关键词： Temporal Difference learning Markov Chain Noise Distributional Convergence

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Deep Sleep Recognition Based on CNNs and Data Augmentation 20th

Deep Sleep Recognition Based on CNNs and Data Augmentation

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20th International conference on artificial intelligence applications and Innovations (AIAI)

作者： Chen, Ruixuan Sui, Linfeng Xia, Mo Cao, Jianting Saitama Inst Technol Grad Sch Engn Fukaya Japan RIKEN Ctr Adv Intelligence Project AIP Tokyo Japan

ISBN: (纸本)9783031632259;9783031632235;9783031632228

Deep sleep is a key part of the sleep cycle and plays a crucial role in the daily physical recovery process. Due to the complexity and lengthy process of collecting sleep EEG data in real life, which can also pose unnecessary inconvenience to subjects over long periods, we employed three data augmentation methods to enrich the dataset and introduce more variability under limited data capabilities. In this paper, we explore the application of data augmentation in classifying deep sleep stages by analyzing electroencephalogram (EEG) signals with Convolutional Neural Networks (CNNs). These strategies are designed to present the machine learning models with a broader range of sleep EEG signal features, thereby enhancing their ability to accurately identify deep sleep stages. The study employed three publicly available CNN models that are effective for sleep stage classification and utilizes the Sleep-EDF public dataset for validation. The research findings indicate that datasets augmented with the proposed techniques show higher classification accuracy across all models, confirming the effectiveness and potential of these data augmentation methods in the context of deep sleep stage identification.

关键词： deep sleep convolutional neural networks data augmentation

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Lifelong learning for Robust AI Systems 4

Lifelong Learning for Robust AI Systems

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Vallabha, Gautam K. Markowitz, Jared Johns Hopkins Univ Appl Phys Lab 11100 Johns Hopkins Rd Laurel MD 20723 USA

ISBN: (纸本)9781510651036;9781510651029

Existing artificial intelligence (AI) agents are most successful on narrow, well-defined tasks, where training data are plentiful, well-labeled, and match the deployment scenarios. It is also possible to train an AI agent to do multiple tasks (such as identifying IEDs from image data as well as recognizing faces), given sufficient time to craft the training regime and network architecture. However, data and time are often in short supply - multi-domain operations involve rapidly shifting and adaptive compositions of capabilities, against adversaries that will likely be adapting on the fly. We suggest that to be robust in such scenarios, AIs need to be capable of learning in the field with opportunistically-available data, limited human oversight, and limited or no access to ground truth. These challenges also apply to reinforcement learning agents, with the additional challenge that for such agents, bad decisions cascade and pose a further difficulty in learning new tasks on the fly. We present an overview of the challenges in enabling AI systems for multi-domain operations, current algorithmic approaches for developing lifelong learning agents, and potential techniques for evaluating them.

关键词： continual learning domain adaptation artificial intelligence catastrophic forgetting evaluation metrics

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Unsupervised Salient Patch Selection for Data-Efficient Reinforcement learning

Unsupervised Salient Patch Selection for Data-Efficient Rein...

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作者： Jiang, Zhaohui Weng, Paul Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China

ISBN: (纸本)9783031434204;9783031434211

To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on Vision Transformer models pre-trained in a self-supervised fashion to reconstruct images from randomly-sampled patches. These pre-trained models can then be exploited to detect and select salient patches, defined as hard to reconstruct from neighboring patches. In RL, the SPIRL agent processes selected salient patches via an attention module. We empirically validate SPIRL on Atari games to test its data-efficiency against relevant state-of-the-art methods, including some traditional model-based methods and keypoint-based models. In addition, we analyze our model's interpretability capabilities.

关键词： image-based deep reinforcement learning data efficiency interpretability keypoint

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Archangel Dataset: UAV-based Imagery with Position and Pose Metadata 4

Archangel Dataset: UAV-based Imagery with Position and Pose ...

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conference on artificial intelligence and machine learning for Multi-Domain Operations applications iv

作者： Lee, Yaesop Lee, Eung-Joo Conover, Damon M. Shen, Yi-Ting Kwon, Heesung Bhattacharyya, Shuvra S. Hill, Jason Evensen, Kenneth Leong, G. Jeremy Univ Maryland ECE Dept College Pk MD 20742 USA Univ Maryland UMIACS College Pk MD 20742 USA MGH Dept Radiol CAMCA Boston MA USA Harvard Med Sch Boston MA 02115 USA DEVCOM Army Res Lab ARL Adelphi MD USA Def Threat Reduct Agcy DTRA Ft Belvoir VA USA

ISBN: (纸本)9781510651036;9781510651029

Object detection on imagery captured onboard aerial platforms involves different challenges than in ground-to-ground object detection. For example, images captured from UAVs with varying altitude and view angles present challenges for machine learning that are due to variations in appearance and scene attributes. Thus, it is essential to closely examine the critical variables that impact object detection from UAV platforms, such as the significant variations in pose, range to objects, background clutter, lighting, weather conditions, and velocity/acceleration of the UAV. To that end, in this work, we introduce a UAV-based image dataset, called the Archangel dataset, collected with a UAV that includes pose and range information in the form of metadata. Additionally, we use the Archangel dataset to conduct comprehensive studies of how the critical attributes of UAV-based images affect machine learning models for object detection. The extensive analysis on the Archangel dataset aims to advance optimal training and testing of machine learning models in general as well as the more specific case of UAV-based object detection using deep neural networks.

关键词： Unmanned aerial vehicles Performance modeling Target detection Cameras machine learning Analytical research

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Sequential Underspecified Instrument Selection for Cause-Effect Estimation 40

Sequential Underspecified Instrument Selection for Cause-Eff...

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40th International conference on machine learning

作者： Ailer, Elisabeth Hartford, Jason Kilbertus, Niki Helmholtz Munich HelmholtzAI Munich Germany Tech Univ Munich Sch Comp Informat & Technol Munich Germany MILA Quebec Artificial Intelligence Inst Montreal PQ Canada Recursion Montreal PQ Canada Munich Ctr Machine Learning Munich Germany

Instrumental variable (iv) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable. Instruments are variables which only affect the outcome indirectly via the treatment variable(s). Most iv applications focus on low-dimensional treatments and crucially require at least as many instruments as treatments. This assumption is restrictive: in the natural sciences we often seek to infer causal effects of high-dimensional treatments (e.g., the effect of gene expressions or microbiota on health and disease), but can only run few experiments with a limited number of instruments (e.g., drugs or antibiotics). In such underspecified problems, the full treatment effect is not identifiable in a single experiment even in the linear case. We show that one can still reliably recover the projection of the treatment effect onto the instrumented subspace and develop techniques to consistently combine such partial estimates from different sets of instruments. We then leverage our combined estimators in an algorithm that iteratively proposes the most informative instruments at each round of experimentation to maximize the overall information about the full causal effect.

关键词： Gene expression

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artificial intelligence Meets Tactical Autonomy: Challenges and Perspectives 4

Artificial Intelligence Meets Tactical Autonomy: Challenges ...

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IEEE 4th International conference on Cognitive machine intelligence (CogMI)

作者： Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci EECS DoD Ctr Excellence Artificial Intelligence & Mach Washington DC 20059 USA

ISBN: (纸本)9781665474061

artificial intelligence (AI) enabled systems have shown tremendous impact in our national defense and in our society due to recent advances in artificial neural networks, deep learning, machine learning, and Internet of Things, big data, computing and communications. New AI capabilities can improve efficiency, trust, and efficacy for mission critical applications for tactical autonomy with minimal supervision from human operators in multi-domain battlefield (MDB) environments that are complex, contested and unpredictable. Although AI-enabled tools have been responsive to people and complementary to human capabilities, in order to realize its full potential in tactical applications, there are several challenges to be addressed for making trustworthy, ethical, fair, real-time explainable AI-enabled autonomous systems. Collaborations between platforms/systems as well as joint human-machine learning/teaming could address many of these issues to provide trusted and shared understanding and delivering cost-effective and adaptive systems to assist operations across military domains (space, air, land, maritime, and cyber) at combat speed using a shared set of resources. In this paper, we present some challenges and perspectives for AI enabled tactical autonomy.

关键词： Tactical autonomy artificial intelligence MDO MDB Advanced Battle Management Systems (ABMS) TEV/V

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Computer network information security monitoring system 2

Computer network information security monitoring system

引用

2nd International conference on Big Data, Computational intelligence, and applications, BDCIA 2024

作者： Lin, Hongkai Wuhan Business University Hubei Wuhan430056 China

ISBN: (纸本)9781510689053

Although traditional protection methods can improve the security of the network to a certain extent, its effect is often limited in the face of complex network environment and various attacks. Therefore, it is an important problem to be solved in the field of communication network security to find a more efficient and intelligent communication network link data security protection method. This paper proposes a computer network information security monitoring system combined with knowledge mapping technology, which aims to improve the data security protection effect of computer communication network. this paper designs and implements an information security situation awareness system, which can find and deal with network security threats in time by monitoring and analyzing network data traffic and security event information in real time, and improves the efficiency and level of information security protection. The results show that the information security situational awareness system based on artificial intelligence technology has high detection accuracy and strong real-time response capability, and can effectively deal with various network security threats. It has a good application prospect in data security protection of communication network link. © 2025 SPIE.

关键词： Network security Data communications Computer security defense and security Information security Data modeling machine learning Computer networks Evolutionary algorithms Education and training

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Integrating AI with Lean Manufacturing in the Context of Industry 4.0/5.0: Current Trends and applications 43rd

Integrating AI with Lean Manufacturing in the Context of Ind...

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43rd IFIP WG 5.7 International conference on Advances in Production Management Systems (APMS)

作者： Boursali, Aze-Eddine Benderbal, Hichem Haddou Souier, Mehdi Higher Sch Appl Sci Tilimsen Algeria Aix Marseille Univ Univ Toulon CNRS Marseille France Univ Tlemcen Mfg Engn Lab Tlemcen MELT Tilimsen Algeria

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This article presents an examination of the integration of artificial intelligence (AI) within lean manufacturing processes across the transformative phases of Industry 4.0 and Industry 5.0. The study focuses on how AI intersects with smart manufacturing, sustainability, metaheuristics, human factors, industry 4.0, and digitalization. It highlights the significant applications of AI in improving maintenance optimization, production efficiency, and quality improvement within lean manufacturing settings. The researchers conducted a swift overview of literature using the SCOPUS database to identify relevant articles through structured keyword searches. The results, presented in a comprehensive table, advocate for more research on integrating sustainable and human-centered manufacturing practices to ensure that technological advancements benefit both productivity and the well-being of human workers.

关键词： lean manufacturing artificial intelligence machine learning metaheuristic sustainability industry 4.0 industry 5.0

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Federated learning of Models Pre-Trained on Different Features with Consensus Graphs 39

Federated Learning of Models Pre-Trained on Different Featur...

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39th conference on Uncertainty in artificial intelligence (UAI)

作者： Ma, Tengfei Hoang, Trong Nghia Chen, Jie IBM Res Yorktown Hts NY 10598 USA Washington State Univ Pullman WA USA MIT IBM Watson AI Lab Cambridge MA USA

learning an effective global model on private and decentralized datasets has become an increasingly important challenge of machine learning when applied in practice. Existing distributed learning paradigms, such as Federated learning, enable this via model aggregation which enforces a strong form of modeling homogeneity and synchronicity across clients. This is however not suitable to many practical scenarios. For example, in distributed sensing, heterogeneous sensors reading data from different views of the same phenomenon would need to use different models for different data modalities. Local learning therefore happens in isolation but inference requires merging the local models to achieve consensus. To enable consensus among local models, we propose a feature fusion approach that extracts local representations from local models and incorporates them into a global representation that improves the prediction performance. Achieving this requires addressing two non-trivial problems. First, we need to learn an alignment between similar feature components which are arbitrarily arranged across clients to enable representation aggregation. Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction. This paper presents solutions to these problems and demonstrates them in real-world applications on time series data such as power grids and traffic networks.

关键词： learning systems

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