Search Results for author:
Found 46 papers, 13 papers with code
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.
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.
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.
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.
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
+5
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.
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.
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.
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
To fairly evaluate the performance, we also propose three specific evaluation metrics.
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.
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.
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.
SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations
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.
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.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Medical report generation is one of the most challenging tasks in medical image analysis.
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.
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.
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.
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.
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.
Reasoning over Multi-view Knowledge Graphs
(ii) It supports complex logical queries with varying relation and view constraints (e. g., with complex topology and/or from multiple views); (iii) It scales up to KGs of large sizes (e. g., millions of facts) and fine-granular views (e. g., dozens of views); (iv) It generalizes to query structures and KG views that are unobserved during training.
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users.
AutoML in The Wild: Obstacles, Workarounds, and Expectations
Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users.
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT
This is a new yet practical scenario in federated learning, i. e., labels-at-server semi-supervised federated learning (SemiFL).
Boosting Few-Shot Text Classification via Distribution Estimation
Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.
Few-Shot Image Classification
Few-Shot Text Classification
+1
On the Security Risks of Knowledge Graph Reasoning
Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).
Hate Speech Detection via Dual Contrastive Learning
Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words.
Zero-Resource Hallucination Prevention for Large Language Models
Existing techniques for hallucination detection in language assistants rely on intricate fuzzy, specific free-language-based chain of thought (CoT) techniques or parameter-based methods that suffer from interpretability issues.
Beyond Co-occurrence: Multi-modal Session-based Recommendation
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR).
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.
Hierarchical Pretraining on Multimodal Electronic Health Records
Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks.
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
Mitigating Pooling Bias in E-commerce Search via False Negative Estimation
Efficient and accurate product relevance assessment is critical for user experiences and business success.
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations.
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions
The widespread adoption of Electronic Health Record (EHR) systems in healthcare institutes has generated vast amounts of medical data, offering significant opportunities for improving healthcare services through deep learning techniques.
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
Correspondingly, we propose a novel VQAttack model, which can iteratively generate both image and text perturbations with the designed modules: the large language model (LLM)-enhanced image attack and the cross-modal joint attack module.
CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning
Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing.
Knowledge-Guided Paraphrase Identification
We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips
We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis.
