Most Downloaded Articles

Open access

ISSN: 2666-6510

Graph neural networks: A review of methods and applications

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface,...

Share article

A survey of transformers

Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots...

Share article

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...

Share article

PTR: Prompt Tuning with Rules for Text Classification

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...

Share article

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...

Share article

Neural machine translation: A review of methods, resources, and tools

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT)...

Share article

Deep learning for fake news detection: A comprehensive survey

The information age enables people to obtain news online through various channels, yet in the meanwhile making false news spread at unprecedented speed. Fake news exerts detrimental effects for it impairs...

Share article

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...

Share article

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...

Share article

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...

Share article

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...

Share article

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...

Share article

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...

Share article

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...

Share article

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,...

Share article

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...

Share article

Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges

Event is a common but non-negligible knowledge type. How to identify events from texts, extract their arguments, even analyze the relations between different events are important for many applications....

Share article

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)...

Share article

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...

Share article

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...

Share article

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...

Share article

CPM-2: Large-scale cost-effective pre-trained language models

In recent years, the size of pre-trained language models (PLMs) has grown by leaps and bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real-world scenarios. We...

Share article

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....

Share article

Domain generalization by class-aware negative sampling-based contrastive learning

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...

Share article

StackVAE-G: An efficient and interpretable model for time series anomaly detection

Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this...

Share article

Stay Informed

Register your interest and receive email alerts tailored to your needs. Sign up below.