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Found 20 papers, 9 papers with code
BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers
Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions.
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining
Our system aims to reduce both the ratio of outlier topics to the total number of topics and the similarity between topic definitions.
Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data
In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data.
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels.
Link Analysis meets Ontologies: Are Embeddings the Answer?
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size.
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually.
Semantic Reasoning from Model-Agnostic Explanations
The derived semantic explanations are potentially more informative, as they describe the key attributes in the context of more general background knowledge, e. g., at the biological process level.
Transfer Learning for Node Regression Applied to Spreading Prediction
This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance.
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
The utility of ReliefE for high-dimensional data sets is ensured by its implementation that utilizes sparse matrix algebraic operations.
Multi-Label Classification
Vocal Bursts Intensity Prediction
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations.
Ranked #17 on
Node Classification
on Coauthor CS
COVID-19 therapy target discovery with context-aware literature mining
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert.
Propositionalization and Embeddings: Two Sides of the Same Coin
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches.
Feature Importance Estimation with Self-Attention Networks
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret.
Symbolic Graph Embedding using Frequent Pattern Mining
The proposed SGE approach on a venue classification task outperforms shallow node embedding methods such as DeepWalk, and performs similarly to metapath2vec, a black-box representation learner that can exploit node and edge types in a given graph.
Embedding-based Silhouette Community Detection
Mining complex data in the form of networks is of increasing interest in many scientific disciplines.
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
Network node embedding is an active research subfield of complex network analysis.
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
The use of background knowledge is largely unexploited in text classification tasks.
Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients
However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis.
A framework for redescription set construction
In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences.
