Generating graph perturbations to enhance the generalization of GNNs
2024
Graph neural networks (GNNs) have become the standard approach for performing machine learning on graphs. Such models need large amounts of training data, however, in several graph classification and...
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Mining contacts from spatio-temporal trajectories
2024
Contact mining is discovering objects in close proximity in their movements in order to reveal possible interactions, infections, collisions or contacts. This process can be significantly beneficial...
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Publisher's Note
2024
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Improving task generalization via unified schema prompt
2023
Task generalization has been a long-standing challenge in Natural Language Processing (NLP). Recent research attempts to improve the task generalization ability of pre-trained language models by mapping...
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Associating multiple vision transformer layers for fine-grained image representation
2023
- Accurate discriminative region proposal has an important effect for fine-grained image recognition. The vision transformer (ViT) brings about a striking effect in computer vision due to its innate...
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Joint span and token framework for few-shot named entity recognition
2023
Few-shot Named Entity Recognition (NER) is a challenging task that involves identifying new entity types using a limited number of labeled instances for training. Currently, the majority of Few-shot...
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MOTT: A new model for multi-object tracking based on green learning paradigm
2023
Multi-object tracking (MOT) is one of the most essential and challenging tasks in computer vision (CV). Unlike object detectors, MOT systems nowadays are more complicated and consist of several neural...
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Multi-grained hypergraph interest modeling for conversational recommendation
2023
Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user’s instant information need....
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A unified network embedding algorithm for multi-type similarity measures
2023
Traditional network embedding aims to learn representations by capturing a predefined vertex-to-vertex similarity measure. However, in practice, there are different types of similarity measures (e.g.,...
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Batch virtual adversarial training for graph convolutional networks
2023
We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs). BVAT addresses the issue that GCNs do not ensure the smoothness of the model’s...
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AdaDS: Adaptive data selection for accelerating pre-trained language model knowledge distillation
2023
Knowledge distillation (KD) is a widely used method for transferring knowledge from large teacher models to computationally efficient student models. Unfortunately, the computational cost of KD becomes...
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A survey on complex factual question answering
2023
Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts, structured knowledge graphs and relational...
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Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation
2023
This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a...
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Learning fair representations via an adversarial framework
2023
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work,...
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Is Chinese Spelling Check ready? Understanding the correction behavior in real-world scenarios
2023
The task of Chinese Spelling Check (CSC) is crucial for identifying and rectifying spelling errors in Chinese texts. While prior work in this domain has predominantly relied on benchmarks such as SIGHAN...
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Semantic graph based topic modelling framework for multilingual fake news detection
2023
Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing...
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Word sense induction with agglomerative clustering and mutual information maximization
2023
Word sense induction (WSI) is a challenging problem in natural language processing that involves the unsupervised automatic detection of a word’s senses (i.e., meanings). Recent work achieves significant...
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Sarcasm detection using news headlines dataset
2023
Sarcasm has been an elusive concept for humans. Due to interesting linguistic properties, sarcasm detection has gained traction of the Natural Language Processing (NLP) research community in the past...
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Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions
2023
Cervical cancer remains a significant health concern worldwide, where precise segmentation of cervical lesions is integral for effective diagnosis and treatment planning. This systematic review critically...
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MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model
2023
Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the...
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Interactive active learning for fairness with partial group label
2023
The rapid development of AI technologies has found numerous applications across various domains in human society. Ensuring fairness and preventing discrimination are critical considerations in the development...
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