Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift.
Graph representations are frequently used in the field of Electronic Design Automation. For example, a combinational circuit can be efficiently modeled as a directed graph to facilitate structure analysis, as shown in Figure 4.6. Sign in to download full-size image FIGURE 4.6. A combinational ...
"Learning attention-based representations from multiple patterns for relation prediction in knowledge graphs". Knowledge-Based Systems 2022. paper (CLGAT-KGC) LinYu Li, Xuan Zhang, YuBin Ma, Chen Gao, Jishu Wang, Yong Yu, Zihao Yuan, Qiuying Ma. "A knowledge graph completion model based on ...
In the field of bioinformatics, k-mers refer to subsequences of length k that are found within gene sequences. To transform a sequence into numerical representations, it is neces- sary to generate k-mers by sliding a fixed-size window of length k. During this process, the DNA sequence is...
In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying seman...
Then, we leverage these representations in a two-phased portfolio optimization setting. First, we select a subset of assets and then weigh the obtained assets. While many portfolio optimization methods do not select the assets explicitly before weighing them, we focus on portfolio selection in this...
Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pangenome graph. Yet, so far, this step
AI* IA 2019–Advances in Artificial Intelligence: XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy, November 19–22, 2019, Proceedings 18, Springer, pp 294–306 Baek J, Kang M, Hwang SJ (2021) Accurate learning of graph representations with ...
80. Both skeleton and tracing representations are ideal for quantifying geometrical features like neurite direction, length, and branching71,81, and may be further refined in post-processing by techniques such as pruning. However, these complementary models do not explicitly consider morphological ...
Kaspar Riesen is especially interested in graph based representations in pattern recognition and related fields. An extended version of Kaspar Riesens PhD thesis appeared as a book by a well-known international publisher. He has more than 40 publications in international peer-reviewed journals, ...