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Found 21 papers, 14 papers with code
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI
To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model.
Multi-Modal Knowledge Graph Transformer Framework for Multi-Modal Entity Alignment
To address these challenges, we propose a novel MMEA transformer, called MoAlign, that hierarchically introduces neighbor features, multi-modal attributes, and entity types to enhance the alignment task.
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI
Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i. e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification.
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.
Reinforcement Learning Guided Multi-Objective Exam Paper Generation
To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria.
Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI
In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data.
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.
Event Extraction by Associating Event Types and Argument Roles
It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
A Survey on Deep Learning Event Extraction: Approaches and Applications
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
Attend and select: A segment selective transformer for microblog hashtag generation
These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.
Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.
Modeling relation paths for knowledge base completion via joint adversarial training
By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.
Knowledge Graph Embedding with Iterative Guidance from Soft Rules
In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules.
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