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ISSN: 2666-6510

PM2.5 forecasting under distribution shift: A graph learning approach

We present a new benchmark task for graph-based machine learning, aiming to predict future air quality (PM2.5 concentration) observed by a geographically distributed network of environmental sensors....

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How to generate popular post headlines on social media?

Posts, as important containers of user-generated-content on social media, are of tremendous social influence and commercial value. As an integral component of post, headline has decisive influence on...

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Enhancing neural network classification using fractional-order activation functions

In this paper, a series of novel activation functions is presented, which is derived using the improved Riemann–Liouville conformable fractional derivative (RLCFD). This study investigates the use of...

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CPT: Colorful Prompt Tuning for pre-trained vision-language models

Vision-Language Pre-training (VLP) models have shown promising capabilities in grounding natural language in image data, facilitating a broad range of cross-modal tasks. However, we note that there...

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Few-shot Named Entity Recognition via encoder and class intervention

In the real world, the large and complex nature of text increases the difficulty of tagging and results in a limited amount of tagged text. Few-shot Named Entity Recognition(NER) only uses a small amount...

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Boosting graph search with attention network for solving the general orienteering problem

Recently, several studies explore to use neural networks(NNs) to solve different routing problems, which is an auspicious direction. These studies usually design an encoder–decoder based framework that...

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An ecosystem for personal knowledge graphs: A survey and research roadmap

This paper presents an ecosystem for personal knowledge graphs (PKG), commonly defined as resources of structured information about entities related to an individual, their attributes, and the relations...

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A survey on complex factual question answering

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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Sarcasm detection using news headlines dataset

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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Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation

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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A unified network embedding algorithm for multi-type similarity measures

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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Semantic graph based topic modelling framework for multilingual fake news detection

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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Graph-based methods for cervical cancer segmentation: Advancements, limitations, and future directions

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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Learning fair representations via an adversarial framework

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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Batch virtual adversarial training for graph convolutional networks

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

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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Restricted orthogonal gradient projection for continual learning

Continual learning aims to avoid catastrophic forgetting and effectively leverage learned experiences to master new knowledge. Existing gradient projection approaches impose hard constraints on the...

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Improving task generalization via unified schema prompt

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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Joint span and token framework for few-shot named entity recognition

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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UPRec: User-aware Pre-training for sequential Recommendation

Recent years witness the success of pre-trained models to alleviate the data sparsity problem in recommender systems. However, existing pre-trained models for recommendation mainly focus on leveraging...

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Associating multiple vision transformer layers for fine-grained image representation

- 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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MOTT: A new model for multi-object tracking based on green learning paradigm

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

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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MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model

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

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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