Adaptive negative representations for graph contrastive learning
2024
Graph contrastive learning (GCL) has emerged as a promising paradigm for learning graph representations. Recently, the idea of hard negatives is introduced to GCL, which can provide more challenging...
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How to generate popular post headlines on social media?
2024
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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PM2.5 forecasting under distribution shift: A graph learning approach
2024
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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Label-aware debiased causal reasoning for Natural Language Inference
2024
Recently, researchers have argued that the impressive performance of Natural Language Inference (NLI) models is highly due to the spurious correlations existing in training data, which makes models...
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Boosting graph search with attention network for solving the general orienteering problem
2024
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
2024
This paper presents an ecosystem for personal knowledge graphs (PKGs), commonly defined as resources of structured information about entities related to an individual, their attributes, and the relations...
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Few-shot Named Entity Recognition via encoder and class intervention
2024
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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CPT: Colorful Prompt Tuning for pre-trained vision-language models
2024
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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Enhancing neural network classification using fractional-order activation functions
2024
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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GPT understands, too
2024
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU). However, our preliminary study reveals that manual discrete prompts...
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Improving trajectory classification through Kramers–Moyal coefficients
2024
Trajectory classification focuses on predicting the class or category of a moving object based on its observed movement over time. The classification of trajectory data using classical approaches can...
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MindLLM: Lightweight large language model pre-training, evaluation and domain application
2024
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial...
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Authorship style transfer with inverse transfer data augmentation
2024
Authorship style transfer aims to modify the style of neutral text to match the unique speaking or writing style of a particular individual. While Large Language Models (LLMs) present promising solutions,...
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Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature
2024
Biomedical knowledge is typically organized in a relational scheme, such as chemical-disease relation, gene-disease relation, and gene-pathway relation. Biomedical scientists heavily rely on search...
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A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations
2024
Deep reinforcement learning (DRL) has been shown to have numerous potential applications in the real world. However, DRL algorithms are still extremely sensitive to noise and adversarial perturbations,...
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Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves
2024
This paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic...
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CellBoost: A pipeline for machine assisted annotation in neuroanatomy
2024
One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent...
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Large language models in law: A survey
2024
The advent of artificial intelligence (AI) has significantly impacted the traditional judicial industry. Moreover, recently, with the development of AI-generated content (AIGC), AI and law have found...
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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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