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Found 31 papers, 20 papers with code
Endowing Protein Language Models with Structural Knowledge
Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules.
Fisher Information Embedding for Node and Graph Learning
Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings.
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input.
Approximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.
Structure-Aware Transformer for Graph Representation Learning
Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.
Ranked #4 on
Graph Property Prediction
on ogbg-code2
Emotion Recognition in Conversation
Graph Representation Learning
Weisfeiler and Leman go Machine Learning: The Story so far
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions
Graph generative models are a highly active branch of machine learning.
Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.
Graph Kernels: State-of-the-Art and Future Challenges
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis.
Challenging Euclidean Topological Autoencoders
Topological autoencoders (TopoAE) have demonstrated their capabilities for performing dimensionality reduction while at the same time preserving topological information of the input space.
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.
Path Imputation Strategies for Signature Models of Irregular Time Series
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
Metropolis Algorithms for Representative Subgraph Sampling
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.
Set Functions for Time Series
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).
A Persistent Weisfeiler–Lehman Procedure for Graph Classification
The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.
Ranked #1 on
Graph Classification
on MUTAG
(Mean Accuracy metric)
Wasserstein Weisfeiler-Lehman Graph Kernels
Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures.
Ranked #7 on
Graph Classification
on NCI1
Topological Autoencoders
We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders.
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.
Finding Statistically Significant Interactions between Continuous Features
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing.
Finding significant combinations of features in the presence of categorical covariates
In high-dimensional settings, where the number of features p is typically much larger than the number of samples n, methods which can systematically examine arbitrary combinations of features, a huge 2^p-dimensional space, have recently begun to be explored.
Halting in Random Walk Kernels
Random walk kernels measure graph similarity by counting matching walks in two graphs.
Searching for significant patterns in stratified data
Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem.
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Multi-task prediction models are widely being used to couple regressors or classification models by sharing information across related tasks.
Rapid Distance-Based Outlier Detection via Sampling
Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data.
Scalable kernels for graphs with continuous attributes
While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.
Geometric tree kernels: Classification of COPD from airway tree geometry
Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.
