The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
Department of Computer Science, University of Freiburg, Germany（弗赖堡大学计算机科学系）；Artificial Intelligence Research Center, AIST, Tokyo, Japan（日本科学技术厅人工智能研究中心）
IBM Research（IBM研究院）；University of Sao Paulo（圣保罗大学）；INSA de Rouen（鲁昂国立应用科学学院）
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicability of those kernels are on machine learning and data science tasks where uncertainty in data has an ontic or epistemistic interpretation.
The University of Manchester（曼彻斯特大学）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯人工智能实验室）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till now, a great variety of Transformers has been proposed to adapt to the graph-structured data. However, a comprehensive literature review and systematical evaluation of these Transformer variants for graphs are still unavailable. It's imperative to sort out the existing Transformer models for graphs and systematically investigate their effectiveness on various graph tasks. In this survey, we provide a comprehensive rev…
Google Research, Brain Team（谷歌研究院大脑团队）；McGill University（麦吉尔大学）；DeepMind
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis, we provide empirical evidence that learning this distance alongside the value function yields structur…
TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）
We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, Mathematics, and Medicin…
Stanford University（斯坦福大学）；University of Toronto（多伦多大学）；University of Tokyo（东京大学）
Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxies and even images. In this work, we investigate the role of learning in the automated discovery of self-similarity and in its utilization for downstream tasks. To this end, we design a novel class of implicit operators, Neural Collages, which (1) represent data as the parameters of a self-referential, structured transformation, and (2) employ hypernetworks to amortize the cos…
ETH Zurich（苏黎世联邦理工学院）；Eindhoven University of Technology（埃因霍温理工大学）；ETH Singapore SEC Ltd（苏黎世联邦理工学院新加坡中心有限公司）
Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence. GDL bears particular promise in molecular modeling applications, in which various molecular representations with different symmetry properties and levels of abstraction exist. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned mole…
Faculty of Informatics, Masaryk University（马萨里克大学信息学院）
In this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Latent Semantic Indexing (LSI). The methods are evaluated on a subset of arXiv.org papers with the Mathematics Subject Classification (MSC) as a reference classification and using the standard precision/recall/F1-measure metrics. The results give insight into how different math representations may influence the performance of the classification and sim…
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Vivocaz
Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The calculation of semantic similarity from ontology has developed and depending on the context is complemented by other similarity calculation methods. In this paper, we propose and carry on an appro…
