Search Results for author:
Found 13 papers, 9 papers with code
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Previous works have tried to tackle this issue in graph classification by providing the model with inductive biases derived from assumptions on the generative process of the graphs, or by requiring access to graphs from the test domain.
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem.
odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks
One of the main complications in studying temporal motifs is the large number of motifs that can be built even with a limited number of vertices or edges.
SPRISS: Approximating Frequent $k$-mers by Sampling Reads, and Applications
The extraction of $k$-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets.
PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts
The analysis of motifs in temporal networks, called temporal motifs, is becoming an important component in the analysis of modern networked datasets.
Are Graph Convolutional Networks Fully Exploiting the Graph Structure?
Graph Convolutional Networks (GCNs) represent the state-of-the-art for many graph related tasks.
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
We show that the embeddings produced by our method can be used to perform multiple tasks with comparable or higher performance than classically trained models.
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining.
The Impact of Global Structural Information in Graph Neural Networks Applications
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
We propose a simple and effective transfer-learning based strategy to adapt a model to a specific user, providing an average increment of $6\%$ on the F1 score on the predictions for that user.
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation
The experiments provide evidence that, unlike other heuristics, our estimation strategy not only provides tight theoretical guarantees but is also able to return highly accurate estimations while running in a fraction of the time required by the exact computation, and that its distributed implementation is highly scalable, thus enabling the computation of internal measures for very large datasets for which the exact computation is prohibitive.
Efficient algorithms to discover alterations with complementary functional association in cancer
The availability of quantitative target profiles, from genetic perturbations or from clinical phenotypes, provides additional information that can be leveraged to improve the identification of cancer related gene sets by discovering groups with complementary functional associations with such targets.
Finding the True Frequent Itemsets
It requires to identify all itemsets appearing in at least a fraction $\theta$ of a transactional dataset $\mathcal{D}$.
