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Relevant bibliographies by topics / Slot value inference

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Academic literature on the topic 'Slot value inference'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Slot value inference"

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Shi, Dan, Chaobin You, Jiantao Huang, Taihao Li, and Deyi Xiong. "CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18952–60. http://dx.doi.org/10.1609/aaai.v38i17.29861.

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As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dataset that contains abundant commonsense knowledge manually annotated on dyadic dialogues, to evaluate the commonsense reasoning and commonsense conflict detection capabilities of Chinese LLMs. We categorize commonsense knowledge in everyday conversations into three dimensions: entity, event, and social interaction. For easy and consistent annotation, we standardize the form of commonsense knowledge annotation in open

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Ismail, Nanang, Iim Nursalim, Hendri Maja Saputra, and Teddy Surya Gunawan. "Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 706. http://dx.doi.org/10.11591/ijeecs.v12.i2.pp706-715.

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Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle

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Nanang, Ismail, Nursalim Iim, Maja Saputra Hendri, and Surya Gunawan Teddy. "Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 706–15. https://doi.org/10.11591/ijeecs.v12.i2.pp706-715.

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Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle

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4

Heck, Michael, Nurul Lubis, Carel van Niekerk, et al. "Robust Dialogue State Tracking with Weak Supervision and Sparse Data." Transactions of the Association for Computational Linguistics 10 (2022): 1175–92. http://dx.doi.org/10.1162/tacl_a_00513.

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Abstract Generalizing dialogue state tracking (DST) to new data is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. Sample sparsity, distributional shift, and the occurrence of new concepts and topics frequently lead to severe performance degradation during inference. In this paper we propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. Two novel input-level dropout methods mitigate the negative impact of sample sparsity. We propose a new model architecture with a unified enco

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Park, Se Jin, Minsu Kim, Joanna Hong, Jeongsoo Choi, and Yong Man Ro. "SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2062–70. http://dx.doi.org/10.1609/aaai.v36i2.20102.

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The challenge of talking face generation from speech lies in aligning two different modal information, audio and video, such that the mouth region corresponds to input audio. Previous methods either exploit audio-visual representation learning or leverage intermediate structural information such as landmarks and 3D models. However, they struggle to synthesize fine details of the lips varying at the phoneme level as they do not sufficiently provide visual information of the lips at the video synthesis step. To overcome this limitation, our work proposes Audio-Lip Memory that brings in visual in

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Collette, Sven, Wolfgang M. Pauli, Peter Bossaerts, and John O'Doherty. "Neural computations underlying inverse reinforcement learning in the human brain." eLife 6 (October 30, 2017). http://dx.doi.org/10.7554/elife.29718.

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In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as o

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Prasad, Ch Devi Vara, P. Kanakaraju, R. Vinu, and Abhijit P. Deshpande. "The Impact of Different Catalysts on Heterogeneous Hydrogenation of Hydroxyl‐Terminated Polybutadiene." ChemistrySelect 9, no. 21 (2024). http://dx.doi.org/10.1002/slct.202401883.

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AbstractHydroxyl‐terminated polybutadiene (HTPB) is hydrogenated using catalytic hydrogenation with HTPB as a precursor using various catalysts. The hydrogenation is carried out using iso‐propyl alcohol (IPA) and toluene as an active solvent media. The present paper used (a) charcoal, (b) palladium supported by charcoal (Pd−C), (c) palladium supported by zirconium oxide (Pd‐ZrO2), and (d) copper chromite (CC) as catalysts for hydrogenation of HTPB to study the influence on the degree of hydrogenation with respect to each catalyst. The experimental data shows that hydrogenated HTPB (HHTPB) form

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Chanda, Panse1 and Dr. Manali Kshirsaga. "SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORK." August 22, 2018. https://doi.org/10.5121/ijbb.2013.3302.

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International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013 DOI: 10.5121/ijbb.2013.3302 13 SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORK Chanda Panse1 and Dr. Manali Kshirsagar1 1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India 1Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India ABSTRACT Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives information about at which different environmental conditions genes

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Dissertations / Theses on the topic "Slot value inference"

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Bouguelia, Sara. "Modèles de dialogue et reconnaissance d'intentions composites dans les conversations Utilisateur-Chatbot orientées tâches." Electronic Thesis or Diss., Lyon 1, 2023. http://www.theses.fr/2023LYO10106.

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Les Systèmes de Dialogue (ou simplement chatbots) sont très demandés de nos jours. Ils permettent de comprendre les besoins des utilisateurs (ou intentions des utilisateurs), exprimés en langage naturel, et de répondre à ces intentions en invoquant les APIs (Interfaces de Programmation d’Application) appropriées. Les chatbots sont connus pour leur interface facile à utiliser et ils ne nécessitent que l'une des capacités les plus innées des humains qui est l'utilisation du langage naturel. L'amélioration continue de l'Intelligence Artificielle (IA), du Traitement du Langage Naturel (NLP) et du

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Book chapters on the topic "Slot value inference"

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Laurel, Jacob, and Sasa Misailovic. "Continualization of Probabilistic Programs With Correction." In Programming Languages and Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_14.

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AbstractProbabilistic Programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximat

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Smith, Elizabeth L., and David Walshaw. "Modelling Bivariate Extremes in a Region." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0048.

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Abstract Practitioners of extreme value methodology have been slow to accept the Bayesian paradigm, and the initial work that has been carried out in recent years has reflected the history of the classical approach, in concentrating solely on univariate problems. In this paper we take a first step towards balancing the substantial frequentist literature on multivariate extreme value inference by considering problems of bivariate inference from a Bayesian point of view. We relate the bivariate case to inference problems for extremes of environmental variables recorded at a number of locations i

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Mareschal, Denis, and Sam Blakeman. "Fast and Slow Learning in Human-Like Intelligence." In Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.003.0016.

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In this chapter we review the extent to which rapid one-short learning or fast-mapping exists in human learning. We find that it exists in both children and adults, but that it is almost always accompanied by slow consolidated learning in which new knowledge is integrated with existing knowledge-bases. Rapid learning is also present in a broad range of non-human species, particularly in the context of high reward values. We argue that reward prediction errors guide the extent to which fast or slow learning dominates, and present a Complementary Learning Systems neural network model (CTDL) of c

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