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Found 8 papers, 0 papers with code
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction
We explore the underlying topologies and patterns in molecular structures by applying Vietoris-Rips persistent homology across varying scales and parameters such as atomic weight, partial charge, bond type, and chirality.
Multiparameter Persistent Homology for Molecular Property Prediction
In this study, we present a novel molecular fingerprint generation method based on multiparameter persistent homology.
Topology-Aware Focal Loss for 3D Image Segmentation
To tackle this problem, we introduce a novel loss function, namely Topology-Aware Focal Loss (TAFL), that incorporates the conventional Focal Loss with a topological constraint term based on the Wasserstein distance between the ground truth and predicted segmentation masks' persistence diagrams.
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG
One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting.
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds.
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks.
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks
Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing.
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
