標題: Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024 [打印本頁] 作者: 和尚吃肉片 時間: 2025-3-21 18:28
書目名稱Neural-Symbolic Learning and Reasoning影響因子(影響力)
書目名稱Neural-Symbolic Learning and Reasoning影響因子(影響力)學科排名
書目名稱Neural-Symbolic Learning and Reasoning網(wǎng)絡(luò)公開度
書目名稱Neural-Symbolic Learning and Reasoning網(wǎng)絡(luò)公開度學科排名
書目名稱Neural-Symbolic Learning and Reasoning被引頻次
書目名稱Neural-Symbolic Learning and Reasoning被引頻次學科排名
書目名稱Neural-Symbolic Learning and Reasoning年度引用
書目名稱Neural-Symbolic Learning and Reasoning年度引用學科排名
書目名稱Neural-Symbolic Learning and Reasoning讀者反饋
書目名稱Neural-Symbolic Learning and Reasoning讀者反饋學科排名
作者: Stricture 時間: 2025-3-21 23:00 作者: Insul島 時間: 2025-3-22 03:10 作者: 行乞 時間: 2025-3-22 06:16
Bayesian Inverse Graphics for?Few-Shot Concept Learninguses our new differentiable renderer for optimizing global scene parameters through gradient descent, sampling posterior distributions over object parameters with Markov Chain Monte Carlo (MCMC), and using a neural based likelihood function. The code and datasets are available at .).作者: Silent-Ischemia 時間: 2025-3-22 10:34 作者: 模仿 時間: 2025-3-22 14:34 作者: Ancestor 時間: 2025-3-22 18:29 作者: Recessive 時間: 2025-3-22 21:46
Enhancing Machine Learning Predictions Through Knowledge Graph Embeddingsechniques, applied to heart and chronic kidney disease prediction. Our results indicate consistent improvements in model performance across various ML models and tasks, thus confirming our hypothesis, e.g. we increased the F2 score for the KNN from 70% to 82.22%, and the F2 score for SVM from 74.53%作者: buoyant 時間: 2025-3-23 03:09 作者: 碎片 時間: 2025-3-23 09:29 作者: instructive 時間: 2025-3-23 11:51 作者: EXULT 時間: 2025-3-23 15:01
Octavio Arriaga,Jichen Guo,Rebecca Adam,Sebastian Houben,Frank Kirchnernd automatic) continuation, how difficult boundary conditions can be handled, and give many examples of how to convert BVPs to standard form. Some BVPs are much better solved using the finite difference methods as explained in the PDE chapter. We give an example of such a boundary value problem at t作者: 口訣 時間: 2025-3-23 18:30
Alessandro Daniele,Tommaso Campari,Sagar Malhotra,Luciano Serafinimined by means of an equivalent circular fin method and segment method, are compared with the results obtained from FEM. Examples that illustrate the computation of a heat transfer coefficient in pipes finned longitudinally and crosswise are presented here as well. Three exercises deal with the way 作者: chandel 時間: 2025-3-24 00:09
Jessica Ciupa,Vaishak Belle all the mathematical derivations and solutions to some of the more significant transient and steady-state heat conduction problems with respect to both, the movable and immovable heat sources and the phenomena of melting and freezing. Lots of attention was paid to non-linear problems. The methods f作者: 的染料 時間: 2025-3-24 03:22
Yaniv Aspis,Mohammad Albinhassan,Jorge Lobo,Alessandra Russo all the mathematical derivations and solutions to some of the more significant transient and steady-state heat conduction problems with respect to both, the movable and immovable heat sources and the phenomena of melting and freezing. Lots of attention was paid to non-linear problems. The methods f作者: fluoroscopy 時間: 2025-3-24 08:53 作者: MEN 時間: 2025-3-24 11:45
Binxia Xu,Antonis Bikakis,Daniel Onah,Andreas Vlachidis,Luke Dickens作者: obscurity 時間: 2025-3-24 14:56 作者: IRATE 時間: 2025-3-24 20:18
Daniel Cunnington,Mark Law,Jorge Lobo,Alessandra Russo作者: AV-node 時間: 2025-3-25 01:53 作者: 小卒 時間: 2025-3-25 04:29
Francesco Manigrasso,Stefan Schouten,Lia Morra,Peter Bloem作者: 一回合 時間: 2025-3-25 08:11 作者: jabber 時間: 2025-3-25 14:31
Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn作者: 溫順 時間: 2025-3-25 17:37
ULLER: A Unified Language for?Learning and?Reasoningng classical FOL, fuzzy logic, and probabilistic logic. We believe . is a first step towards making NeSy research more accessible and comparable, paving the way for libraries that streamline training and evaluation across a multitude of semantics, knowledge bases, and NeSy systems.作者: 生氣地 時間: 2025-3-25 22:23
Disentangling Visual Priors: Unsupervised Learning of?Scene Interpretations with?Compositional Autoeonfront our approach with a baseline method on a synthetic benchmark and demonstrate its capacity to disentangle selected aspects of the image formation process, learn from small data, correct inference in the presence of noise, and out-of-sample generalization.作者: Discrete 時間: 2025-3-26 03:50
Terminating Differentiable Tree Experts to make automatically. The resulting Terminating Differentiable Tree Experts model sluggishly learns to predict the number of steps without an oracle. It can do so while maintaining the learning capabilities of the model, converging to the optimal amount of steps.作者: APNEA 時間: 2025-3-26 04:41 作者: BUOY 時間: 2025-3-26 09:58 作者: CRUC 時間: 2025-3-26 14:32
0302-9743 ombining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their.complementary weaknesses..978-3-031-71166-4978-3-031-71167-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: FACET 時間: 2025-3-26 20:30 作者: 牲畜欄 時間: 2025-3-26 22:54
0302-9743 eld in Barcelona, Spain during September 9-12th, 2024...The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI.?Neurosymbolic AI aims to build rich computational models and systems by c作者: 流行 時間: 2025-3-27 03:13 作者: cataract 時間: 2025-3-27 06:38
Ethical Reward Machineting ethical principles does not increase runtime. Therefore, our results suggest that ethical considerations do not substantially burden computational resources. Ultimately, the overarching objective is to develop and validate a learning framework that ensures AI alignment with human learning and ethical policies.作者: armistice 時間: 2025-3-27 11:55
ViPro: Enabling and?Controlling Video Prediction for?Complex Dynamical Scenarios Using Procedural Kngs, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a symbolically addressable interface between data-driven aspects in the model and our dedicated procedural knowledge module, which we utilize in downstream control tasks.作者: 自然環(huán)境 時間: 2025-3-27 15:23 作者: giggle 時間: 2025-3-27 18:35 作者: Esalate 時間: 2025-3-27 23:22 作者: Arthropathy 時間: 2025-3-28 05:07
Gudmund Grov,Jonas Halvorsen,Magnus Wiik Eckhoff,Bj?rn Jervell Hansen,Martin Eian,Vasileios Mavroeidons, a very important branch of mathematics. Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial dif作者: BOLT 時間: 2025-3-28 08:13
Octavio Arriaga,Jichen Guo,Rebecca Adam,Sebastian Houben,Frank Kirchneris . package have an interface which is similar to the interface of the initial value problem solvers in the package .. The default input to the solvers is very simple, requiring specification of only one function that calculates the derivatives while the boundary conditions are represented as simpl作者: 收集 時間: 2025-3-28 11:59
Alessandro Daniele,Tommaso Campari,Sagar Malhotra,Luciano Serafinig of heat transfer coefficients for multi-layered flat and cylindrical partitions, the determination of a quasi-steady-state temperature field and the computation of a radiant tube temperature in boilers. Critical thickness of thermal insulation on the surface of the cylindrical tube is first determ作者: 不連貫 時間: 2025-3-28 16:13 作者: 具體 時間: 2025-3-28 21:00 作者: installment 時間: 2025-3-29 01:26
Olga Mashkova,Fernando Zhapa-Camacho,Robert Hoehndorfhe specific computational problems.Focuses on a new language.The goal of this new edition is the same as for the first edition ”to?address the fault detection and isolation topics from a computational perspective“,?by covering the same important aspects, namely, (1) providing a completely general?th作者: RAG 時間: 2025-3-29 05:29
Context Helps: Integrating Context Information with?Videos in?a?Graph-Based HAR Frameworke-of-the-art (SoTA) models rely heavily on domain specific supervised fine-tuning of visual features, and even with this data- and compute-intensive fine-tuning, overall performance can still be limited. We argue that the next generation of HAR models could benefit from explicit neuro-symbolic mecha作者: Thyroxine 時間: 2025-3-29 08:48 作者: 釋放 時間: 2025-3-29 13:55
Variable Assignment Invariant Neural Networks for?Learning Logic Programssymbolic algorithms, but they are unable to deal with noise or generalize to unobserved transitions. Rule extraction based neural network methods suffer from overfitting, while more general implementation that categorize rules suffer from combinatorial explosion. In this paper, we introduce a techni作者: enflame 時間: 2025-3-29 18:25
ViPro: Enabling and?Controlling Video Prediction for?Complex Dynamical Scenarios Using Procedural Knh of data-driven models. On the basis of new challenging scenarios we show that state-of-the-art video predictors struggle in complex dynamical settings, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a sy作者: SOB 時間: 2025-3-29 22:17 作者: 陪審團每個人 時間: 2025-3-30 00:16 作者: 先鋒派 時間: 2025-3-30 05:51
On the?Use of?Neurosymbolic AI for?Defending Against Cyber Attacksectionist and symbolic AI are currently being used to support such detection and response. In this paper, we make the case for combining them using neurosymbolic AI. We identify a set of challenges when using AI today and propose a set of neurosymbolic use cases we believe are both interesting resea作者: N斯巴達人 時間: 2025-3-30 12:06 作者: Custodian 時間: 2025-3-30 14:28 作者: 無情 時間: 2025-3-30 19:42 作者: 小歌劇 時間: 2025-3-31 00:27 作者: TRACE 時間: 2025-3-31 03:01
ULLER: A Unified Language for?Learning and?Reasoningnow are a wide variety of NeSy frameworks, each with its own specific language for expressing background knowledge and how to relate it to neural networks. This heterogeneity hinders accessibility for newcomers and makes comparing different NeSy frameworks challenging. We propose a unified language 作者: entrance 時間: 2025-3-31 06:18
Disentangling Visual Priors: Unsupervised Learning of?Scene Interpretations with?Compositional Autoe transforms, and other higher-level structures. We propose a neurosymbolic architecture that uses a domain-specific language to capture selected priors of image formation, including object shape, appearance, categorization, and geometric transforms. We express template programs in that language and 作者: LEER 時間: 2025-3-31 11:17 作者: jabber 時間: 2025-3-31 15:18
Enhancing Machine Learning Predictions Through Knowledge Graph Embeddingsby insufficient training data and poor data quality, with particularly severe consequences in critical areas such as medical diagnosis prediction. Our hypothesis is that enhancing ML pipelines with semantic information such as those available in knowledge graphs (KG) can address these challenges and作者: CIS 時間: 2025-3-31 18:45
Terminating Differentiable Tree Expertsor Product Representations. We investigate the architecture and propose two key components. We first remove a series of different transformer layers that are used in every step by introducing a mixture of experts. This results in a Differentiable Tree Experts model with a constant number of paramete作者: 不安 時間: 2025-4-1 00:27
