Relevant bibliographies by topics / Interpretable By Design Architectures

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Interpretable By Design Architectures'

Author: Grafiati
Published: 7 December 2024
Last updated: 19 July 2025

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Journal articles on the topic "Interpretable By Design Architectures"

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Pasupuleti, Murali Krishna. "Supersymmetric Quantum Neural Networks: Bridging Superalgebras and AI Architectures." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 48–59. https://doi.org/10.62311/nesx/rp0425.

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Abstract: This paper proposes a novel paradigm for quantum artificial intelligence: the design and implementation of Supersymmetric Quantum Neural Networks (S-QNNs) that explicitly integrate superalgebraic structures into quantum circuit-based AI architectures. By embedding the symmetry principles of supersymmetry—captured by quantum superalgebras such as osp(1∣2) and sl(1∣1)—into the computational fabric of quantum neural networks, we aim to create models that exhibit both structural elegance and computational efficiency. The resulting framework allows for interpretable, energy-efficient, and mathematically grounded learning systems that leverage both fermionic (discrete) and bosonic (continuous) quantum states to encode and process information. This interdisciplinary approach opens new directions for building explainable and scalable quantum AI systems, particularly in the era of Noisy Intermediate-Scale Quantum (NISQ) devices. Keywords: Supersymmetric Neural Networks, Quantum Superalgebras, Quantum AI, Hybrid Quantum-Classical Learning, osp(1|2), sl(1|1), Interpretable AI, Quantum Symmetry, NISQ
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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 (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 (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, et al. "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection." PLOS Computational Biology 18, no. 9 (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 (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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Geng, Xinyu, Jiaming Wang, Xiaolin Huang, Fanglin Chen, and Jun Xu. "ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3122–30. https://doi.org/10.1609/aaai.v39i3.32321.

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Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks often underperform due to their shallow structures, and deeper variants lack hierarchical architectures, thereby compromising interpretability. This paper introduces a novel capsule network, ParseCaps, which utilizes the sparse axial attention routing and parse convolutional capsule layer to form a parse-tree-like structure, enhancing both depth and interpretability. Firstly, sparse axial attention routing optimizes connections between child and parent capsules, as well as emphasizes the weight distribution across instantiation parameters of parent capsules. Secondly, the parse convolutional capsule layer generates capsule predictions aligning with the parse tree. Finally, based on the loss design that is effective whether concept ground truth exists or not, ParseCaps advances interpretability by associating each dimension of the global capsule with a comprehensible concept, thereby facilitating clinician trust and understanding of the model's classification results. Experimental results on three medical datasets show that ParseCaps not only outperforms other capsule network variants in classification accuracy and robustness, but also provides interpretable explanations, regardless of the availability of concept labels.
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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 (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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Benfaress, Ilyass, Afaf Bouhoute, and Ahmed Zinedine. "Advancing Traffic Sign Recognition: Explainable Deep CNN for Enhanced Robustness in Adverse Environments." Computers 14, no. 3 (2025): 88. https://doi.org/10.3390/computers14030088.

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This paper presents a traffic sign recognition (TSR) system based on the deep convolutional neural network (CNN) architecture, which proves to be extremely accurate in recognizing traffic signs under challenging conditions such as bad weather, low-resolution images, and various environmental-impact factors. The proposed CNN is compared with other architectures, including GoogLeNet, AlexNet, DarkNet-53, ResNet-34, VGG-16, and MicronNet-BF. Experimental results confirm that the proposed CNN significantly improves recognition accuracy compared to existing models. In order to make our model interpretable, we utilize explainable AI (XAI) approaches, specifically Gradient-weighted Class Activation Mapping (Grad-CAM), that can give insight into how the system comes to its decision. The evaluation of the Tsinghua-Tencent 100K (TT100K) traffic sign dataset showed that the proposed method significantly outperformed existing state-of-the-art methods. Additionally, we evaluated our model on the German Traffic Sign Recognition Benchmark (GTSRB) dataset to ensure generalization, demonstrating its ability to perform well in diverse traffic sign conditions. Design issues such as noise, contrast, blurring, and zoom effects were added to enhance performance in real applications. These verified results indicate both the strength and reliability of the CNN architecture proposed for TSR tasks and that it is a good option for integration into intelligent transportation systems (ITSs).
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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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Dissertations / Theses on the topic "Interpretable By Design Architectures"

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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<br>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.<br>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.<br>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.<br>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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Books on the topic "Interpretable By Design Architectures"

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Cpałka, Krzysztof. Design of Interpretable Fuzzy Systems. 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. (Name) Publications, 2009.

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

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Minoli, Daniel. Internet architectures. 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. Maggioli editore, 2019.

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

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

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

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

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Book chapters on the topic "Interpretable By Design Architectures"

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

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Vaucher, Cicero. "Synthesizer Architectures." In Analog Circuit Design. 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. 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. 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. Routledge, 2019. http://dx.doi.org/10.4324/9780429264306-3.

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Self, Douglas. "Preamplifier Architectures." In Small Signal Audio Design. Focal Press, 2020. http://dx.doi.org/10.4324/9781003031833-7.

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Sundström, Lars. "Linear Transmitter Architectures." In Analog Circuit Design. 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. Macmillan Education UK, 2002. http://dx.doi.org/10.1007/978-1-4039-1466-8_3.

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Shiva, Sajjan G. "Advanced Architectures." In Computer Organization, Design, and Architecture, 6th ed. CRC Press, 2025. https://doi.org/10.1201/9781003497202-12.

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

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Conference papers on the topic "Interpretable By Design Architectures"

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Mills, Keith G., Fred X. Han, Mohammad Salameh, et al. "Building Optimal Neural Architectures Using Interpretable Knowledge." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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). 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). 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}. 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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Costamagna, Andrea, Alan Mishchenko, Satrajit Chatterjee, and Giovanni De Micheli. "Symmetry-Based Synthesis for Interpretable Boolean Evaluation." In 2025 38th International Conference on VLSI Design and 2025 24th International Conference on Embedded Systems (VLSID). IEEE, 2025. https://doi.org/10.1109/vlsid64188.2025.00077.

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

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Iwuchukwu, Vitalis C., Counsel I. Okpabi, Lilian C. Okpalla, et al. "Design Of An Interpretable Model For Disease Diagnosis Among University Students." In 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON). IEEE, 2024. https://doi.org/10.1109/nigercon62786.2024.10926943.

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Hirushan, A. M. Nadun, and S. P. Kasthuri Arachchi. "Integrate Interpretable AI into Chest X ray Diagnosis through a Comparative Analysis of CNN Architectures with Grad CAM Visualizations." In 2024 International Conference on Advances in Technology and Computing (ICATC). IEEE, 2024. https://doi.org/10.1109/icatc64549.2024.11025232.

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Dragoni, Arianna, and Alessandro Margara. "Semi-Automated Design of Data-Intensive Architectures." In 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C). IEEE, 2025. https://doi.org/10.1109/icsa-c65153.2025.00047.

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Wang, Wenbo, Tao Xue, Shuailou Li, Zhaoyang Wang, Boyang Zhang, and Yu Wen. "Interpretable Risk-aware Access Control for Spark: Blocking Attack Purpose Behind Actions." In 2024 IEEE 42nd International Conference on Computer Design (ICCD). IEEE, 2024. https://doi.org/10.1109/iccd63220.2024.00028.

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Reports on the topic "Interpretable By Design Architectures"

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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, 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. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada266523.

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

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Meng, Teresa H. Asynchronous Design for Parallel Processing Architectures. Defense Technical Information Center, 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. Defense Technical Information Center, 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), 2015. http://dx.doi.org/10.2172/1311761.

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Weisgraber, Todd H., Ward Small, Jeremy M. Lenhardt, et al. Universal Design Curves for Elastomeric Direct Ink-Writing Architectures. Office of Scientific and Technical Information (OSTI), 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. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada397589.

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Mudunuru, Maruti, James Ang, Halappanavar Mahentesh, et al. Perspectives on AI Architectures and Co-design for Earth System Predictability. Office of Scientific and Technical Information (OSTI), 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), 2024. http://dx.doi.org/10.2172/2463004.

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