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检索条件"任意字段=19th International Conference on Algorithmic Learning Theory"

共 547 条记录，以下是1-10 订阅

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Logic Operators and Quantifiers in Type-theory of Algorithms 1

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19th international conference on Logic and Engineering of Natural Language Semantics (LENLS)

作者： Loukanova, Roussanka Bulgarian Acad Sci Inst Math & Informat Acad G Bonchev StBlock 8 Sofia 1113 Bulgaria

ISBN: (数字)9783031439773

ISBN: (纸本)9783031439766;9783031439773

In this work, I introduce the Type-theory of Algorithms (TTA), which is an extension of Moschovakis Type-theory of Algorithms and its reduction calculus, by adding logic operators and quantifiers. the formal language has two kinds of terms of formulae, for designating state-independent and state-dependent propositions and predications. the logic operators include conjunction, disjunction, conditional implication, and negation. I add state-dependent quantifiers, for enhancing the standard quantifiers of predicate logic. I provide an extended reduction calculus of the Type-theory of Acyclic Algorithms, for reductions of terms to their canonical forms. the canonical forms of the terms provide the algorithmic semantics for computing the denotations.

关键词： recursion type-theory acyclic algorithms denotational semantics algorithmic semantics reduction calculus logic operators quantifiers

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Responsible Artificial Intelligence and Bias Mitigation in Deep learning Systems 27

Responsible Artificial Intelligence and Bias Mitigation in D...

引用

27th international conference on Information Visualisation (IV) / 19th international conference Computer Graphics, Imaging and Visualization (CGiV)

作者： Gavrilova, Marina L. Univ Calgary Comp Sci Dept Calgary AB Canada

ISBN: (纸本)9798350341614

Responsible, ethical and trustworthy decision making powered by the new generation of artificial intelligence (AI) and deep learning (DL) recently emerged as one of the key societal challenges. the tutorial discusses key challenges, lists major applications, presents mitigation strategies and provides insights on the future developments in this emerging research domain.

关键词： Artificial Intelligence Deep learning responsible AI ethics algorithmic fairness bias mitigation visual knowledge discovery biometrics decision-making

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PRET19: Automatic Recognition and Indexing of Handwritten Loan Registers from 19th Century Parisian Universities 28th

PRET19: Automatic Recognition and Indexing of Handwritten Lo...

引用

28th international conference on Linking theory and Practice of Digital Libraries (TPDL)

作者： Perissier, Lea Rebolledo-Dhuin, Viera Petiot, Marie-therese Schneider, Yoann Kermorvant, Christopher Bibliotheque Interuniv La Sorbonne Paris France Univ Paris Est Creteil Ctr Rech Hist Europeenne Comparee Paris France TEKLIA Paris France

ISBN: (纸本)9783031724367;9783031724374

the PRET19 project aims to carry out a comprehensive analysis of the nineteenth-century loan registers of several Parisian university libraries. these historical documents provide invaluable insights into the circulation of books and the intellectual engagement of the academic community during a transformative period. By reconstructing the relationships and trends between borrowers, the project offers a unique perspective on the intellectual landscape of Parisian universities. In this first phase, the registers come from three different university libraries and exhibit a great diversity in layout, handwriting and content, which poses significant challenges for data processing. To address these challenges, we developed a document processing workflow that effectively combines automatic handwritten text recognition (HTR) with manual processing and validation. In addition, we provide a detailed description of the database, which is designed to comprehensively model the information extracted from the registries, ensuring that the data is structured in a way that adequately responds to anticipated research requests. Once completed, the processed data will be from winter 2024 accessible via a dedicated website, providing a comprehensive digital resource for historical research.

关键词： Librairies Loans Registers Paris 19th Century HTR Deep learning

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Efficient Agnostic learning with Average Smoothness 35

Efficient Agnostic Learning with Average Smoothness

引用

35th international conference on algorithmic learning theory

作者： Hanneke, Steve Kontorovich, Aryeh Kornowski, Guy Purdue Univ W Lafayette IN 47907 USA Ben Gurion Univ Negev Beer Sheva Israel Weizmann Inst Sci Rehovot Israel

We study distribution-free nonparametric regression following a notion of average smoothness initiated by Ashlagi et al. (2021), which measures the "effective" smoothness of a function with respect to an arbitrary unknown underlying distribution. While the recent work of Hanneke et al. (2023) established tight uniform convergence bounds for average-smooth functions in the realizable case and provided a computationally efficient realizable learning algorithm, both of these results currently lack analogs in the general agnostic (i.e. noisy) case. In this work, we fully close these gaps. First, we provide a distribution-free uniform convergence bound for average-smoothness classes in the agnostic setting. Second, we match the derived sample complexity with a computationally efficient agnostic learning algorithm. Our results, which are stated in terms of the intrinsic geometry of the data and hold over any totally bounded metric space, show that the guarantees recently obtained for realizable learning of average-smooth functions transfer to the agnostic setting. At the heart of our proof, we establish the uniform convergence rate of a function class in terms of its bracketing entropy, which may be of independent interest.

关键词： agnostic learning average smoothness bracketing numbers generalization metric space

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Active learning enhanced deep-learning surrogate model for fast MEMS design with high-dimensional design parameter spaces 19

Active learning enhanced deep-learning surrogate model for f...

引用

IEEE 19th international conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Wang, Chenzi Feng, Lihong Lu, Wenshuai Bian, Wei You, Zheng Benner, Peter Tsinghua Univ Dept Precis Instrument Beijing Peoples R China Max Planck Inst Dynam Complex Tech Syst Dept Computat Methods Syst & Control Theory Magdeburg Germany

ISBN: (纸本)9798350359848;9798350359831

MEMS devices suffer from errors in manufacturing processes such as lithography, etching, thinning, etc. these errors cause non-negligible deviations between the actual device performance and the ideal performance obtained from finite element method (FEM) simulation in the design stage. Often, parametric sweeps via FEM simulations at heuristically selected geometrical and material parameter samples are implemented, in order to analyze the corresponding device performance deviations. this analysis is empirical and often fails due to large number of possible design parameter variations caused by manufacturing. Performing parametric sweeps for all possible parameter instances would result in an exponential increase in FEM simulations. In this paper, we propose a novel active learning enhanced deep-learning surrogate model, which replaces the FEM simulation and faithfully predicts the device performance deviations under numerous parameter variances. this provides a powerful tool for predicting device yield at the design stage. Furthermore, the proposed active learning technique could be applied to efficient training of deep learning models for other MEMS design problems.

关键词： MEMS Design Active learning Surrogate modeling Deep learning High-dimensional parameter spaces

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Comparative Analysis of Machine learning Algorithms and Ensemble Techniques for Diverse Image Classification Tasks 19

Comparative Analysis of Machine Learning Algorithms and Ense...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Salim, Yulita Rakasyah, Athar Fathana Darwis, Herdianti Harlinda Irawati Manga, Abdul Rachman Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. this research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. the study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. these datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. the image classes within these datasets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. the results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. this article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. these findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution. © 2025 IEEE.

关键词： Adversarial machine learning

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On the Complexity of learning Programs 19th

On the Complexity of Learning Programs

引用

19th international conference on Unity of Logic and Computation (CiE)

作者： Brattka, Vasco Univ Bundeswehr Munchen Fac Comp Sci Neubiberg Germany Univ Cape Town Dept Math & Appl Math Rondebosch South Africa

ISBN: (纸本)9783031369773;9783031369780

Given a computable sequence of natural numbers, it is a natural task to find a Godel number of a program that generates this sequence. It is easy to see that this problem is neither continuous nor computable. In algorithmic learning theory this problem is well studied from several perspectives and one question studied there is for which sequences this problem is at least learnable in the limit. Here we study the problem on all computable sequences and we classify the Weihrauch complexity of it. For this purpose we can, among other methods, utilize the amalgamation technique known from learning theory. As a benchmark for the classification we use closed and compact choice problems and their jumps on natural numbers, which correspond to induction and boundedness principles, as they are known from the Kirby-Paris hierarchy in reverse mathematics. We provide a topological as well as a computability-theoretic classification, which reveal some significant differences.

关键词： Artificial intelligence

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19th international conference on Tangible Embedded and Embodied Interaction

作者： Liscio, Christopher Brown, Daniel G. Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9798400711978

Popular musicians often learn songs by ear, from recordings, using technology that provides control over playback events. However, we know little about these human-recording interactions, and our research goal is to develop novel and useful ones. Guided by a preliminary study of 18 YouTube videos of musicians learning by ear, we performed a grounded-theory analysis of 28 YouTube lessons and an instructional DVD that teaches by-ear learning. Based on our findings, we characterize the sub-tasks musicians follow to learn songs from recordings, their variations, and discuss how memory plays a role in ear learning. Armed with these insights, we offer recommendations for the design of four novel, memory-oriented human-recording interactions-facilitating active listening sessions, restricting playback to fit working memory, extracting musical sequences for memorization, and re-synthesizing the recording to play notes indefinitely-all of which are grounded in real-world observations, and studies in both neuroscience and psychology.

关键词： learning music by ear popular music education YouTube content analysis digital ethnography

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Building Each Other Up: Using Variation theory to Build and Correct Human Mental Models of Robots 24

Building Each Other Up: Using Variation Theory to Build and ...

引用

19th Annual ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Horter, Tifany MIT CSAIL Wellesley Coll Cambridge MA 02139 USA

ISBN: (纸本)9798400703232

As we begin to coexist with robots, we must be able to anticipate each other's future actions to avoid dangerous or inefficient situations. How can we build up human and robot conceptual models to predict how each other will behave? To do this, I study how to apply human concept learning theories, like variation theory, to human-robot interaction. I have found that the contrast step of variation theory (showing contrasting examples of policies when learning about robot motions) improves people's ability to predict motion in unseen settings. For my proposed PhD, I seek to connect this idea of improving a human's model of the robot with enabling the robot to correct the human's model or alter its own behavior based on its model of the shortcomings in the human's understanding.

关键词： Mental models Robot motion Variation theory Cognitive science

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theory-Guided Feature Selection in Cybercrime Data Science

Theory-Guided Feature Selection in Cybercrime Data Science

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19th international conference on Cyber Warfare and Security

作者： Naidoo, Rennie Naidoo, Shiven Univ Witwatersrand Johannesburg South Africa

ISBN: (纸本)9781914587962;9781914587979

Cybercrime data science is being significantly hampered by the presence of 'noisy' features within vast and complex datasets. We draw from the theoretical insights of the behavioural sciences to propose a feature selection model to enrich and improve the value and interpretability of cybercrime intelligence datasets. We piloted our theory-guided feature selection approach on a subset of intelligence datafeeds provided by a global fraud and cybercrime tracking firm. the results of the proposed social influence feature selection model show significant improvement in the interpretability of the machine learning-based exploratory analysis and advanced visualization techniques in an experimental setting. the feature selection model yielded rich insights about cybercriminal psychological tactics from social engineering scam data and has potential applicability in the areas of cyberthreat response and cybersecurity awareness training. Our study shows the value of an interdisciplinary theory-guided approach to cybercrime data analytics that integrates scientific knowledge from the behavioural sciences and data science expertise. Our paper concludes by suggesting avenues for future research on theory-guided feature selection seeking to incorporate behavioural science knowledge in cybercrime data science. We intend to refine, automate, evaluate, and scale our model in future research to assess its effectiveness in producing insights about cybercriminal activities and informing decision-making in a naturalistic and real-time setting. In future research efforts, we aim to automate the encoding of features and apply a wider range of machine learning tools and evaluation metrics to extract more meaningful insights into cybercriminal psychological tactics. We also intend to refine our model on larger datasets to enhance its efficiency and responsiveness to real-time cybercrime data. We call on data scientists and cybercrime domain experts to work together to apply theory-guided fe

关键词： Behavioural science Feature selection Cybercrime data analytics Cybersecurity theory-Guided data science

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