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Found 23 papers, 5 papers with code
Knowledge-Guided Paraphrase Identification
We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.
Predicting Ulnar Collateral Ligament Injury in Rookie Major League Baseball Pitchers
In the growing world of machine learning and data analytics, scholars are finding new and innovative ways to solve real-world problems.
Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
Finally, we predict user actions based on item features and users' price and interest preferences.
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare
Deep neural networks (DNNs) have been broadly adopted in health risk prediction to provide healthcare diagnoses and treatments.
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Medical report generation is one of the most challenging tasks in medical image analysis.
ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection
With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach.
SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
Fairness-aware Outlier Ensemble
Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks
Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite.
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.
A Benchmark Dataset for Understandable Medical Language Translation
In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data
However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
To tackle this challenging task, we propose a cross-graph representation learning framework, i. e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently.
Weak Supervision for Fake News Detection via Reinforcement Learning
In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
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Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
Long-Term Memory Networks for Question Answering
Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task.
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results.
