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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Restricted orthogonal gradient projection for continual learning
2023
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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UPRec: User-aware Pre-training for sequential Recommendation
2023
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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GPT understands, too
Available online 26 August 2023
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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CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0
2022
Legal information extraction requires identifying and classifying legal elements from specific legal documents. Considering that information extraction is mainly regarded as the first step in natural...
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HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation
2022
The mainstream domain adaptation (DA) methods transfer the supervised source domain knowledge to the unsupervised or semi-supervised target domain, so as to assist the classification task in the target...
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Optimized separable convolution: Yet another efficient convolution operator
2022
The convolution operation is the most critical component in recent surge of deep learning research. Conventional 2D convolution needs O(C2K2) parameters to represent, where C is the channel size and...
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Survey: Transformer based video-language pre-training
2022
Inspired by the success of transformer-based pre-training methods on natural language tasks and further computer vision tasks, researchers have started to apply transformer to video processing. This...
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Human motion modeling with deep learning: A survey
2022
The aim of human motion modeling is to understand human behaviors and create reasonable human motion like real people given different priors. With the development of deep learning, researchers tend...
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The road from MLE to EM to VAE: A brief tutorial
2022
Variational Auto-Encoders (VAEs) have emerged as one of the most popular genres of generative models, which are learned to characterize the data distribution. The classic Expectation Maximization (EM)...
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Domain generalization by class-aware negative sampling-based contrastive learning
2022
When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy...
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BCA: Bilinear Convolutional Neural Networks and Attention Networks for legal question answering
2022
The National Judicial Examination of China is an essential examination for selecting legal practitioners. In recent years, people have tried to use machine learning algorithms to answer examination...
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A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources
2022
As an important way to alleviate information overload, a recommender system aims to filter out irrelevant information for users and provides them items that they may be interested in. In recent years,...
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Data augmentation approaches in natural language processing: A survey
2022
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language...
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Hierarchical label with imbalance and attributed network structure fusion for network embedding
2022
Network embedding (NE) aims to learn low-dimensional vectors for nodes while preserving the network’s essential properties (e.g., attributes and structure). Previous methods have been proposed to learn...
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Learning towards conversational AI: A survey
2022
Recent years have witnessed a surge of interest in the field of open-domain dialogue. Thanks to the rapid development of social media, large dialogue corpus from the Internet builds up a fundamental...
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Self-directed machine learning
2022
Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive,...
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On the distribution alignment of propagation in graph neural networks
2022
Graph neural networks (GNNs) have been widely adopted for modeling graph-structure data. Most existing GNN studies have focused on designing different strategies to propagate information over the graph...
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PTR: Prompt Tuning with Rules for Text Classification
2022
Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some...
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