Готові списки джерел за темами / Interpretable By Design Architectures

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Добірка наукової літератури з теми "Interpretable By Design Architectures"

Автор: Grafiati

Опубліковано: 7 грудня 2024

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Статті в журналах з теми "Interpretable By Design Architectures"

Zhang, Xinyu, Vincent C. S. Lee, Jia Rong, Feng Liu, and Haoyu Kong. "Multi-channel convolutional neural network architectures for thyroid cancer detection." PLOS ONE 17, no. 1 (January 21, 2022): e0262128. http://dx.doi.org/10.1371/journal.pone.0262128.

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Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians’ adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians’ trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.

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Xie, Nan, and Yuexian Hou. "MMIM: An Interpretable Regularization Method for Neural Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15933–34. http://dx.doi.org/10.1609/aaai.v35i18.17963.

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In deep learning models, most of network architectures are designed artificially and empirically. Although adding new structures such as convolution kernels in CNN is widely used, there are few methods to design new structures and mathematical tools to evaluate feature representation capabilities of new structures. Inspired by ensemble learning, we propose an interpretable regularization method named Minimize Mutual Information Method(MMIM), which minimize the generalization error by minimizing the mutual information of hidden neurons. The experimental results also verify the effectiveness of our proposed MMIM.

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Di Gioacchino, Andrea, Jonah Procyk, Marco Molari, John S. Schreck, Yu Zhou, Yan Liu, Rémi Monasson, Simona Cocco, and Petr Šulc. "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection." PLOS Computational Biology 18, no. 9 (September 29, 2022): e1010561. http://dx.doi.org/10.1371/journal.pcbi.1010561.

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Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures.

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Feinauer, Christoph, Barthelemy Meynard-Piganeau, and Carlo Lucibello. "Interpretable pairwise distillations for generative protein sequence models." PLOS Computational Biology 18, no. 6 (June 23, 2022): e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.

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Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise models from more complex ones using an energy-based modeling framework. We show that for the tested models the extracted pairwise models can replicate the energies of the original models and are also close in performance in tasks like mutational effect prediction. In addition, we show that even simpler, factorized models often come close in performance to the original models.

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Zhang, Zizhao, Han Zhang, Long Zhao, Ting Chen, Sercan Ö. Arik, and Tomas Pfister. "Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3417–25. http://dx.doi.org/10.1609/aaai.v36i3.20252.

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Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer. The benefits of the proposed judiciously-selected design are threefold: (1) NesT converges faster and requires much less training data to achieve good generalization on both ImageNet and small datasets like CIFAR; (2) when extending our key ideas to image generation, NesT leads to a strong decoder that is 8 times faster than previous transformer-based generators; and (3) we show that decoupling the feature learning and abstraction processes via this nested hierarchy in our design enables constructing a novel method (named GradCAT) for visually interpreting the learned model. Source code is available https://github.com/google-research/nested-transformer.

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Gao, Xinjian, Tingting Mu, John Yannis Goulermas, Jeyarajan Thiyagalingam, and Meng Wang. "An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts." IEEE Transactions on Image Processing 29 (2020): 3911–26. http://dx.doi.org/10.1109/tip.2020.2965275.

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Liu, Hao, Youchao Sun, Xiaoyu Wang, Honglan Wu, and Hao Wang. "NPFormer: Interpretable rotating machinery fault diagnosis architecture design under heavy noise operating scenarios." Mechanical Systems and Signal Processing 223 (January 2025): 111878. http://dx.doi.org/10.1016/j.ymssp.2024.111878.

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Sturm, Patrick Obin, and Anthony S. Wexler. "Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)." Geoscientific Model Development 15, no. 8 (April 28, 2022): 3417–31. http://dx.doi.org/10.5194/gmd-15-3417-2022.

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Abstract. Models of atmospheric phenomena provide insight into climate, air quality, and meteorology and provide a mechanism for understanding the effect of future emissions scenarios. To accurately represent atmospheric phenomena, these models consume vast quantities of computational resources. Machine learning (ML) techniques such as neural networks have the potential to emulate computationally intensive components of these models to reduce their computational burden. However, such ML surrogate models may lead to nonphysical predictions that are difficult to uncover. Here we present a neural network architecture that enforces conservation laws to numerical precision. Instead of simply predicting properties of interest, a physically interpretable hidden layer within the network predicts fluxes between properties which are subsequently related to the properties of interest. This approach is readily generalizable to physical processes where flux continuity is an essential governing equation. As an example application, we demonstrate our approach on a neural network surrogate model of photochemistry, trained to emulate a reference model that simulates formation and reaction of ozone. We design a physics-constrained neural network surrogate model of photochemistry using this approach and find that it conserves atoms as they flow between molecules while outperforming two other neural network architectures in terms of accuracy, physical consistency, and non-negativity of concentrations.

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Jacob, Stefan, and Christian Koch. "Unveiling weak auditory evoked potentials using data-driven filtering." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A141. http://dx.doi.org/10.1121/10.0023054.

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Auditory evoked potentials (AEPs) are commonly used to objectively evaluate sound perception in humans. Close to the hearing threshold and for low frequencies, efficient filtering of AEP from other brain activities is of major concern due to weak potentials and the requirement of long averaging times. Filtered AEP data are well-interpretable and useful, especially in medical and psychological diagnostics. Here, we present two data-driven approaches for efficient AEP filtering. First, neural networks of different architectures trained for EEG denoising are used to extract weak late-response AEP for low-frequency and infrasonic stimuli. During the design of the networks, we leveraged knowledge of the specific characteristics of the expected AEP data. Second, a singular value decomposition (SVD) of EEG data is evaluated, attempting to create classifiers for the presence of weak late-response AEP modes. We anticipate that the evaluation of AEP with data-driven methods can support researchers and scientists, for example, with real-time evaluation and diagnosis of acoustic-induced discomfort.

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Zhou, Shuhui. "An exploration of KANs and CKANs for more efficient deep learning architecture." Applied and Computational Engineering 83, no. 1 (September 27, 2024): 20–25. http://dx.doi.org/10.54254/2755-2721/83/2024glg0060.

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Deep learning has revolutionized the field of machine learning with its ability to discern complex patterns from voluminous data. Despite the success of Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), there is an ongoing quest for architectures that offer higher expressiveness with fewer parameters. This paper focuses on the Kolmogorov-Arnold Networks (KANs) and Convolutional Kolmogorov-Arnold Networks (CKANs), which integrate learnable spline functions for enhanced expressiveness and efficiency. This study designs a range of networks to compare KANs with MLPs and CKANs with classical CNNs on the CIFAR-10 dataset. Moreover, this study evaluates the models based on several metrics, including accuracy, precision, recall, F1 score, and parameter count. Based on the experimental results, networks with KANs and CKANs demonstrated improved accuracy with a reduced parameter footprint, indicating the potential of KAN-based models in capturing complex patterns. In conclusion, integrating KANs into CNNs and MLPs is a promising approach for developing more efficient and interpretable models, offering a path to advance deep learning architectures.

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Дисертації з теми "Interpretable By Design Architectures"

Jeanneret, Sanmiguel Guillaume. "Towards explainable and interpretable deep neural networks." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC229.

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Les architectures neuronales profondes ont démontré des résultats remarquables dans diverses tâches de vision par ordinateur. Cependant, leur performance extraordinaire se fait au détriment de l'interprétabilité. En conséquence, le domaine de l'IA explicable a émergé pour comprendre réellement ce que ces modèles apprennent et pour découvrir leurs sources d'erreur. Cette thèse explore les algorithmes explicables afin de révéler les biais et les variables utilisés par ces modèles de boîte noire dans le contexte de la classification d'images. Par conséquent, nous divisons cette thèse en quatre parties. Dans les trois premiers chapitres, nous proposons plusieurs méthodes pour générer des explications contrefactuelles. Tout d'abord, nous incorporons des modèles de diffusion pour générer ces explications. Ensuite, nous lions les domaines de recherche des exemples adversariaux et des contrefactuels pour générer ces derniers. Le suivant chapitre propose une nouvelle méthode pour générer des contrefactuels en mode totalement boîte noire, c'est-à-dire en utilisant uniquement l'entrée et la prédiction sans accéder au modèle. La dernière partie de cette thèse concerne la création de méthodes interprétables par conception. Plus précisément, nous étudions comment étendre les transformeurs de vision en architectures interprétables. Nos méthodes proposées ont montré des résultats prometteurs et ont avancé la frontière des connaissances de la littérature actuelle sur l'IA explicable
Deep neural architectures have demonstrated outstanding results in a variety of computer vision tasks. However, their extraordinary performance comes at the cost of interpretability. As a result, the field of Explanable AI has emerged to understand what these models are learning as well as to uncover their sources of error. In this thesis, we explore the world of explainable algorithms to uncover the biases and variables used by these parametric models in the context of image classification. To this end, we divide this thesis into four parts. The first three chapters proposes several methods to generate counterfactual explanations. In the first chapter, we proposed to incorporate diffusion models to generate these explanations. Next, we link the research areas of adversarial attacks and counterfactuals. The next chapter proposes a new pipeline to generate counterfactuals in a fully black-box mode, \ie, using only the input and the prediction without accessing the model. The final part of this thesis is related to the creation of interpretable by-design methods. More specifically, we investigate how to extend vision transformers into interpretable architectures. Our proposed methods have shown promising results and have made a step forward in the knowledge frontier of current XAI literature

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Kumar, Rakesh. "Holistic design for multi-core architectures." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3222991.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed September 20, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 182-193).

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Poyias, Kyriakos. "Design-by-contract for software architectures." Thesis, University of Leicester, 2014. http://hdl.handle.net/2381/28924.

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We propose a design by contract (DbC) approach to specify and maintain architectural level properties of software. Such properties are typically relevant in the design phase of the development cycle but may also impact the execution of systems. We give a formal framework for specifying software architectures (and their refi nements) together with contracts that architectural con figurations abide by. In our framework, we can specify that if an architecture guarantees a given pre- condition and a refi nement rule satisfi es a given contract, then the refi ned architecture will enjoy a given post-condition. Methodologically, we take Architectural Design Rewriting (ADR) as our architectural description language. ADR is a rule-based formal framework for modelling (the evolution of) software architectures. We equip the recon figuration rules of an ADR architecture with pre- and post-conditions expressed in a simple logic; a pre-condition constrains the applicability of a rule while a post-condition specifi es the properties expected of the resulting graphs. We give an algorithm to compute the weakest precondition out of a rule and its post-condition. Furthermore, we propose a monitoring mechanism for recording the evolution of systems after certain computations, maintaining the history in a tree-like structure. The hierarchical nature of ADR allows us to take full advantage of the tree-like structure of the monitoring mechanism. We exploit this mechanism to formally defi ne new rewriting mechanisms for ADR reconfi guration rules. Also, by monitoring the evolution we propose a way of identifying which part of a system has been a ffected when unexpected run-time behaviours emerge. Moreover, we propose a methodology that allows us to select which rules can be applied at the architectural level to reconfigure a system so to regain its architectural style when it becomes compromised by unexpected run-time recon figurations.

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Shao, Yakun. "Design and Modeling of Specialized Architectures." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493560.

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Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. However, architectural research in the area of specialization architectures is still in its preliminary stages. A major obstacle for such research has been the lack of an architecture-level infrastructure that analyzes and quantifies the benefits and trade-offs across different designs options. Existing accelerator design primarily relies on creating Register-Transfer Level (RTL) implementations, a tedious and time-consuming process, making early-stage, design space exploration for specialized architecture designs infeasible. This dissertation presents the case for early-stage, architecture-level design method- ologies in specialized architecture design and modeling. Starting with workload characterization, the proposed ISA-independent workload characterization approach demonstrates its capability to capture application’s intrinsic characteristics without being biased due to micro-architecture and ISA artifacts. Moreover, to speed up the accelerator design process, this dissertation presents a new modeling methodology for quickly and accurately estimating accelerator power, performance, and area without RTL generation. Aladdin, as a working example of this methodology, is 100× faster than the existing RTL-based simulation, and yet maintains accuracy within 7% of RTL implementations. Finally, accelerators are only part of the entire System on a Chip (SoC). To accurately capture the interactions across CPUs, accelerators, and shared resources, we developed an integrated SoC simulator based on Aladdin to enable system architects to study system-level ramifications of accelerator integration. The techniques presented in this thesis demonstrate some initial steps towards early-stage, architecture-level infrastructures for specialized architectures. We hope that this work, and the other research in the area of accelerator modeling and design, will open up the field of specialized architectures to a wider range of researchers, unlocking new opportunities for efficient accelerator design.
Engineering and Applied Sciences - Computer Science

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Davies, Daniel. "Representation of multiple engineering viewpoints in Computer Aided Design through computer-interpretable descriptive markup." Thesis, University of Bath, 2008. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488893.

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The aim of this work was to find a way of representing multiple interpretations of a product design with the same CAD model and in a way that allowed reduction of the manual work of producing the viewpoint specific models of the product through automation The approach presented is the recording of multiple viewpoint-interpretations of a product design with a CAD product model using descriptive, by-reference (stand-off) computer interpretable markup of the model.

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Ippolito, Corey A. "Software architectures for flight simulation." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15749.

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Nuzman, Joseph. "Memory subsystem design for explicit multithreading architectures." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/146.

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Thesis (M.S.) -- University of Maryland, College Park, 2003.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Smith, Richard Bartlett. "Design and integrity of deterministic system architectures." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445115/.

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Architectures represented by system construction 'building block' components and interrelationships provide the structural form. This thesis addresses processes, procedures and methods that support system design synthesis and specifically the determination of the integrity of candidate architectural structures. Particular emphasis is given to the structural representation of system architectures, their consistency and functional quantification. It is a design imperative that a hierarchically decomposed structure maintains compatibility and consistency between the functional and realisation solutions. Complex systems are normally simplified by the use of hierarchical decomposition so that lower level components are precisely defined and simpler than higher-level components. To enable such systems to be reconstructed from their components, the hierarchical construction must provide vertical intra-relationship consistency, horizontal interrelationship consistency, and inter-component functional consistency. Firstly, a modified process design model is proposed that incorporates the generic structural representation of system architectures. Secondly, a system architecture design knowledge domain is proposed that enables viewpoint evaluations to be aggregated into a coherent set of domains that are both necessary and sufficient to determine the integrity of system architectures. Thirdly, four methods of structural analysis are proposed to assure the integrity of the architecture. The first enables the structural compatibility between the 'building blocks' that provide the emergent functional properties and implementation solution properties to be determined. The second enables the compatibility of the functional causality structure and the implementation causality structure to be determined. The third method provides a graphical representation of architectural structures. The fourth method uses the graphical form of structural representation to provide a technique that enables quantitative estimation of performance estimates of emergent properties for large scale or complex architectural structures. These methods have been combined into a procedure of formal design. This is a design process that, if rigorously executed, meets the requirements for reconstructability.

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Roomi, Akeel S. "Multiprocessor computer architectures : algorithmic design and applications." Thesis, Loughborough University, 1989. https://dspace.lboro.ac.uk/2134/10872.

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The contents of this thesis are concerned with the implementation of parallel algorithms for solving partial differential equations (POEs) by the Alternative Group EXplicit (AGE) method and an investigation into the numerical inversion of the Laplace transform on the Balance 8000 MIMO system. Parallel computer architectures are introduced with different types of existing parallel computers including the Data-Flow computer and VLSI technology which are described from both the hardware and implementation points of view. The main characteristics of the Sequent parallel computer system at Loughborough University is presented, and performance indicators, i.e., the speed-up and efficiency factors are defined for the measurement of parallelism in the system. Basic ideas of programming such computers are also outlined.....

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Dasgupta, Sohini. "Formal design and synthesis of GALS architectures." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446196.

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Книги з теми "Interpretable By Design Architectures"

Cpałka, Krzysztof. Design of Interpretable Fuzzy Systems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52881-6.

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Roberto, Feo, and Hurtado Rosario, eds. Abandon architectures. [Miami?]: (Name) Publications, 2009.

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Penny, Nii, and United States. National Aeronautics and Space Administration., eds. Software design by reusing architectures. Stanford, Calif: Knowledge Systems Laboratory, Dept. of Computer Science, Stanford University, 1992.

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Penny, Nii K., and United States. National Aeronautics and Space Administration., eds. Software design by reusing architectures. Stanford, Calif: Knowledge Systems Laboratory, Dept. of Computer Science, Stanford University, 1992.

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Minoli, Daniel. Internet architectures. New York: Wiley, 1999.

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Poletti, Linda. Interpretare e progettare: La rigenerazione partecipata dagli spazi scolastici per l'infanzia : il caso della Scuola Cadorna a Milano. Santarcangelo di Romagna (RN): Maggioli editore, 2019.

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Ciminiera, Luigi. Advanced microprocessor architectures. Wokingham, England: Addison-Wesley Pub. Co., 1987.

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Svetlana, Kartashev, and Kartashev Steven I, eds. Supercomputing systems: Architectures, design, and performance. New York: Van Nostrand Reinhold, 1990.

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France, Fondation électricité de, Institut français d'architecture, and Espace Electra, eds. Architectures de l'électricité. Paris: Norma, 1992.

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France, Fondation Electricite de, Institute Francais d'Architecture, and L'Espace Electra, eds. Architectures de l'electricite. Paris: NORMA, 1992.

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Частини книг з теми "Interpretable By Design Architectures"

Perumal, Boominathan, Swathi Jamjala Narayanan, and Sangeetha Saman. "Explainable Deep Learning Architectures for Product Recommendations." In Explainable, Interpretable, and Transparent AI Systems, 226–55. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003442509-13.

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Vaucher, Cicero. "Synthesizer Architectures." In Analog Circuit Design, 291–329. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2602-2_14.

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Anker, Peder. "Computing environmental design." In Computer Architectures, 15–34. Milton Park, Abingdon, Oxon : New York, NY : Routledge, 2020 | Series: Routledge research in design, technology: Routledge, 2019. http://dx.doi.org/10.4324/9780429264306-2.

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Tang, Antony, Minh H. Tran, Jun Han, and Hans van Vliet. "Design Reasoning Improves Software Design Quality." In Quality of Software Architectures. Models and Architectures, 28–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87879-7_2.

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Brennan, AnnMarie. "The work of design and the design of work." In Computer Architectures, 35–57. Milton Park, Abingdon, Oxon : New York, NY : Routledge, 2020 | Series: Routledge research in design, technology: Routledge, 2019. http://dx.doi.org/10.4324/9780429264306-3.

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Self, Douglas. "Preamplifier Architectures." In Small Signal Audio Design, 193–98. Third edition. | Abingdon, Oxon ; New York, NY : Routledge, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9781003031833-7.

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Sundström, Lars. "Linear Transmitter Architectures." In Analog Circuit Design, 303–23. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/0-306-47950-8_15.

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Lano, Kevin, José Luiz Fiadeiro, and Luís Andrade. "Software Architectures." In Software Design Using Java 2, 50–86. London: Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-1-4039-1466-8_3.

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Lafi, Walid, and Didier Lattard. "3D Architectures." In Design Technology for Heterogeneous Embedded Systems, 321–38. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1125-9_15.

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Ahmed, Imran. "ADC Architectures." In Pipelined ADC Design and Enhancement Techniques, 7–17. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8652-5_2.

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Тези доповідей конференцій з теми "Interpretable By Design Architectures"

Mills, Keith G., Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, and Di Niu. "Building Optimal Neural Architectures Using Interpretable Knowledge." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5726–35. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00547.

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Cerasuolo, Francesco, Idio Guarino, Vincenzo Spadari, Giuseppe Aceto, and Antonio Pescapé. "XAI for Interpretable Multimodal Architectures with Contextual Input in Mobile Network Traffic Classification." In 2024 IFIP Networking Conference (IFIP Networking), 757–62. IEEE, 2024. http://dx.doi.org/10.23919/ifipnetworking62109.2024.10619769.

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Razak, Tajul Rosli, Jonathan M. Garibaldi, and Christian Wagner. "A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612113.

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Pluska, Alexander, Pascal Welke, Thomas Gärtner, and Sagar Malhotra. "Logical Distillation of Graph Neural Networks." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}, 920–30. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/86.

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We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections between the expressivity of GNNs and the two-variable fragment of first-order logic with counting quantifiers (C2). We introduce a decision-tree based model which leverages an extension of C2 to distill interpretable logical classifiers from GNNs. We test our approach on multiple GNN architectures. The distilled models are interpretable, succinct, and attain similar accuracy to the underlying GNN. Furthermore, when the ground truth is expressible in C2, our approach outperforms the GNN.

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Rana, Amrita, and Kyung Ki Kim. "Tailoring Backbone Architectures for SSD." In 2024 21st International SoC Design Conference (ISOCC), 388–89. IEEE, 2024. http://dx.doi.org/10.1109/isocc62682.2024.10762601.

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Chapman, Martin, and Vasa Curcin. "A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools." In IEEE EUROCON 2019 -18th International Conference on Smart Technologies. IEEE, 2019. http://dx.doi.org/10.1109/eurocon.2019.8861830.

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Fusco, Francesco, Michalis Vlachos, Vasileios Vasileiadis, Kathrin Wardatzky, and Johannes Schneider. "RecoNet: An Interpretable Neural Architecture for Recommender Systems." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/325.

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Neural systems offer high predictive accuracy but are plagued by long training times and low interpretability. We present a simple neural architecture for recommender systems that lifts several of these shortcomings. Firstly, the approach has a high predictive power that is comparable to state-of-the-art recommender approaches. Secondly, owing to its simplicity, the trained model can be interpreted easily because it provides the individual contribution of each input feature to the decision. Our method is three orders of magnitude faster than general-purpose explanatory approaches, such as LIME. Finally, thanks to its design, our architecture addresses cold-start issues, and therefore the model does not require retraining in the presence of new users.

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Wang, Liwei, Suraj Yerramilli, Akshay Iyer, Daniel Apley, Ping Zhu, and Wei Chen. "Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71570.

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Abstract Scientific and engineering problems often require an inexpensive surrogate model to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners in surrogate modeling, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets with over 104 data points, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

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Dolar, Tuba, Doksoo Lee, and Wei Chen. "Interpretable Neural Network Analyses for Understanding Complex Physical Interactions in Engineering Design." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-115103.

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Abstract In engineering design, global sensitivity analysis (GSA) is used for analyzing the effects of the system inputs on the model response. Common GSA methods use analytical or surrogate models to study the relationships between model inputs and outputs. However, the accuracy of such system models depends on the complete conformance to all model assumptions. Even so, they are not flexible and fail to capture nonlinear behaviors in complex systems. Besides these GSA approaches, interpretable machine learning would also identify the relationships between system variables, eliminating the disadvantages of common GSA implementations. Apart from studying the independent variables individually, the evaluation of groups of them is likewise valuable. One example motivation in engineering design for performing GSA with groups of input variables would be managing the design space complexity in programmable material systems (PMS) development. In this article, we employ a flexible, interpretable artificial neural network model to uncover individual as well as grouped global sensitivity indices for understanding complex physical interactions in engineering design. The employed model allows the investigation of the feature importance of the main effects and pairwise interaction effects in GSA according to functional analysis of variance (ANOVA) decomposition. To draw a higher-level understanding, we further use a subset decomposition method to analyze the significance of the groups of input variables. Using PMS as an example, we demonstrate the use of our approach for understanding the impact of material, architecture, and stimulus variables as well as their interactions for a programmable photonic metasurface system. This information lays the foundation for deriving design guidelines for PMS development.

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Ye, Zhen, Wei Xue, Xu Tan, Qifeng Liu, and Yike Guo. "NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis Based on Frequency Modulation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/651.

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Developing digital sound synthesizers is crucial to the music industry as it provides a low-cost way to produce high-quality sounds with rich timbres. Existing traditional synthesizers often require substantial expertise to determine the overall framework of a synthesizer and the parameters of submodules. Since expert knowledge is hard to acquire, it hinders the flexibility to quickly design and tune digital synthesizers for diverse sounds. In this paper, we propose ``NAS-FM'', which adopts neural architecture search (NAS) to build a differentiable frequency modulation (FM) synthesizer. Tunable synthesizers with interpretable controls can be developed automatically from sounds without any prior expert knowledge and manual operating costs. In detail, we train a supernet with a specifically designed search space, including predicting the envelopes of carriers and modulators with different frequency ratios. An evolutionary search algorithm with adaptive oscillator size is then developed to find the optimal relationship between oscillators and the frequency ratio of FM. Extensive experiments on recordings of different instrument sounds show that our algorithm can build a synthesizer fully automatically, achieving better results than handcrafted synthesizers. Audio samples are available at https://nas-fm.github.io/

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Звіти організацій з теми "Interpretable By Design Architectures"

Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.

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Meng, Teresa H. Y. Asynchronous Design for Parallel Processing Architectures. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada266523.

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Meng, Teresa H. Asynchronous Design for Parallel Processing Architectures. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada237696.

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Meng, Teresa H. Asynchronous Design for Parallel Processing Architectures. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada230374.

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Akers, Lex A., Mark R. Walker, and Siamack Haghighi. Design and Training of Limited-Interconnect Architectures. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada251598.

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Carothers, Christopher. Enabling Co-Design of Multi-Layer Exascale Storage Architectures. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1311761.

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Weisgraber, Todd H., Ward Small, Jeremy M. Lenhardt, Thomas Metz, Christopher M. Spadaccini, Robert S. Maxwell, Eric B. Duoss, and Thomas S. Wilson. Universal Design Curves for Elastomeric Direct Ink-Writing Architectures. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1455400.

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Parhi, Keshab K. Design Tools and Architectures for Dedicated Digital Signal Processing (DSP) Processors. Fort Belvoir, VA: Defense Technical Information Center, July 1996. http://dx.doi.org/10.21236/ada397589.

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Mudunuru, Maruti, James Ang, Halappanavar Mahentesh, Simon Hammond, Gokhale Maya, James Hoe, Sreepathi Sarat, Norman Matthew, Ivy Peng, and Philip Jones. Perspectives on AI Architectures and Co-design for Earth System Predictability. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/2378014.

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Maccarone, Lee, Michael Rowland, Robert Brulles, and Andrew Hahn. Design of Defensive Cybersecurity Architectures for High Temperature, Gas-Cooled Reactors. Office of Scientific and Technical Information (OSTI), August 2024. http://dx.doi.org/10.2172/2463004.

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