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ISSN: 2666-6510

The road from MLE to EM to VAE: A brief tutorial

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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Human motion modeling with deep learning: A survey

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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Survey: Transformer based video-language pre-training

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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Learning towards conversational AI: A survey

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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A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources

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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Self-directed machine learning

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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Data augmentation approaches in natural language processing: A survey

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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Advances and challenges in conversational recommender systems: A survey

Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to...

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Neural, symbolic and neural-symbolic reasoning on knowledge graphs

Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can...

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Robustness of deep learning models on graphs: A survey

Machine learning (ML) technologies have achieved significant success in various downstream tasks, e.g., node classification, link prediction, community detection, graph classification and graph clustering....

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Know what you don't need: Single-Shot Meta-Pruning for attention heads

Deep pre-trained Transformer models have achieved state-of-the-art results over a variety of natural language processing (NLP) tasks. By learning rich language knowledge with millions of parameters,...

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The information propagation model of Weibo network based on spiking neural P systems

Information propagation models in the Weibo network play a primary role in analyzing user behaviors, obtaining the propagation paths, determining the opinion leaders, and discovering the hot spots of...

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Discrete and continuous representations and processing in deep learning: Looking forward

Discrete and continuous representations of content (e.g., of language or images) have interesting properties to be explored for the understanding of or reasoning with this content by machines. This...

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WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models

Using large-scale training data to build a pre-trained language model (PLM) with a larger volume of parameters can significantly improve downstream tasks. For example, OpenAI trained the GPT3 model...

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A comprehensive survey of entity alignment for knowledge graphs

Knowledge Graphs (KGs), as a structured human knowledge, manage data in an ease-of-store, recognizable, and understandable way for machines and provide a rich knowledge base for different artificial...

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Lawformer: A pre-trained language model for Chinese legal long documents

Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success...

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CPM: A large-scale generative Chinese Pre-trained language model

Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570 GB training data, drew a lot of attention due to...

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CokeBERT: Contextual knowledge selection and embedding towards enhanced pre-trained language models

Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs), and achieved consistent improvements on...

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Structure-enhanced meta-learning for few-shot graph classification

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle...

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Incorporating bidirectional interactive information and regional features for relational facts extraction

Extracting entity and relation jointly is often complicated since the relational triplets may be overlapped. In this paper, we propose a novel unified joint extraction model that considers the significant...

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A comprehensive review on resolving ambiguities in natural language processing

Natural language processing is a known technology behind the development of some widely known AI assistants such as: SIRI, Natasha, and Watson. However, NLP is a diverse technology used for numerous...

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Network representation learning: A macro and micro view

Abstract...

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Heterogeneous graph knowledge enhanced stock market prediction

We focus on the task of stock market prediction based on financial text which contains information that could influence the movement of stock market. Previous works mainly utilize a single semantic...

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Pre-trained models: Past, present and future

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training...

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Rule-based data augmentation for knowledge graph embedding

Knowledge graph (KG) embedding models suffer from the incompleteness issue of observed facts. Different from existing solutions that incorporate additional information or employ expressive and complex...

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