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Found 13 papers, 3 papers with code
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization
In this work, we carefully analyze the existing methods in heterogeneous environments.
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation
Since it is not always easy to find such binding sites based on domain knowledge or traditional methods, different deep learning methods that predict binding sites out of protein structures have been developed in recent years.
Phase-shifted Adversarial Training
Comprehensive results show that PhaseAT significantly improves the convergence for high-frequency information.
Improving group robustness under noisy labels using predictive uncertainty
In the END framework, we first train the \textit{identification model} to obtain the SCF samples from a training set using its predictive uncertainty.
In Defense of Core-set: A Density-aware Core-set Selection for Active Learning
The strategy is to estimate the density of the unlabeled samples and select diverse samples mainly from sparse regions.
Improving evidential deep learning via multi-task learning
In the MTL, we define the Lipschitz modified mean squared error (MSE) loss function as another loss and add it to the existing NLL loss.
An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention
In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process.
Controlled Molecule Generator for Optimizing Multiple Chemical Properties
Generating a novel and optimized molecule with desired chemical properties is an essential part of the drug discovery process.
Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
Recent advances in deep learning have facilitated the demand of neural models for real applications.
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Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission
One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature.
Classification of Radiology Reports Using Neural Attention Models
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value.
Lexicon Integrated CNN Models with Attention for Sentiment Analysis
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting.
