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Found 26 papers, 10 papers with code
Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network
Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models.
Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
We extract spatio-temporal features from CMR scans using tensor-based learning.
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X, G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset.
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained Alignment
Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data.
SkelEx and BoundEx: Natural Visualization of ReLU Neural Networks
That is why we introduce SkelEx, an algorithm to extract a skeleton of the membership functions learned by ReLU NNs, making those functions easier to interpret and analyze.
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
In this work, we develop a tensor learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic Resonance Imaging (MRI).
Multimodal Learning for Multi-Omics: A Survey
However, several challenges are hindering the development in this area, including the availability of easily accessible open-source tools.
Interpretable bilinear attention network with domain adaptation improves drug-target prediction
Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution.
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
In this paper, we propose to incorporate the knowledge graph (KG) for CDR, which enables items in different domains to share knowledge.
Uncertainty Estimation for Heatmap-based Landmark Localization
We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds.
Evaluating Transformers for Lightweight Action Recognition
Our experiments conclude that current video transformers are not yet capable of lightweight action recognition on par with traditional convolutional baselines, and that the previously mentioned shortcomings need to be addressed to bridge this gap.
Channel-Temporal Attention for First-Person Video Domain Adaptation
Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures.
VideoLightFormer: Lightweight Action Recognition using Transformers
Efficient video action recognition remains a challenging problem.
Team PyKale (xy9) Submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition
Under the team name xy9, our submission achieved 5th place in terms of top-1 accuracy for verb class and all top-5 accuracies.
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
Hop-Hop Relation-aware Graph Neural Networks
Graph Neural Networks (GNNs) are widely used in graph representation learning.
Unifying Homophily and Heterophily Network Transformation via Motifs
Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated.
GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
The use of drug combinations often leads to polypharmacy side effects (POSE).
Ranked #1 on
Pose Prediction
on SUN-Mem
Domain Independent SVM for Transfer Learning in Brain Decoding
We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks.
Mixed-Order Spectral Clustering for Networks
This paper proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both second-order and third-order structures simultaneously, with two MOSC methods developed based on Graph Laplacian (GL) and Random Walks (RW).
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available.
Graph Node-Feature Convolution for Representation Learning
Graph convolutional network (GCN) is an emerging neural network approach.
Semi-Orthogonal Multilinear PCA with Relaxed Start
However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthogonality constraint.
