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Found 59 papers, 16 papers with code
Modularized Syntactic Neural Networks for Sentence Classification
In existing works, aggregating on a syntax tree usually considers local information of sub-trees.
Metaphor Detection via Linguistics Enhanced Siamese Network
In this paper we present MisNet, a novel model for word level metaphor detection.
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings
The results reveal that these grammatical gender-neutral Chinese word embeddings show a certain gender bias, which is consistent with the mainstream society’s perception and judgment of gender.
Cross-modal Contrastive Attention Model for Medical Report Generation
Medical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently.
中文自然语言处理多任务中的职业性别偏见测量(Measurement of Occupational Gender Bias in Chinese Natural Language Processing Tasks)
“尽管悲观者认为, 职场中永远不可能存在性别平等。但随着人们观念的转变, 愈来愈多的人们相信, 职业的选择应只与个人能力相匹配, 而不应由个体的性别决定。目前已经发现自然语言处理的各个任务中都存在着职业性别偏见。但这些研究往往只针对特定的英文任务, 缺乏针对中文的、综合多任务的职业性别偏见测量研究。本文基于霍兰德职业模型, 从中文自然语言处理中常见的三个任务出发, 测量了词向量、共指消解和文本生成中的职业性别偏见, 发现不同任务中的职业性别偏见既有一定的共性, 又存在着独特的差异性。总体来看, 不同任务中的职业性别偏见反映了现实生活中人们对于不同性别所选择职业的刻板印象。此外, 在设计不同任务的偏见测量指标时, 还需要考虑如语体、词序等语言学要素的影响。”
SaGE: 基于句法感知图卷积神经网络和ELECTRA的中文隐喻识别模型(SaGE: Syntax-aware GCN with ELECTRA for Chinese Metaphor Detection)
“隐喻是人类语言中经常出现的一种特殊现象, 隐喻识别对于自然语言处理各项任务来说具有十分基础和重要的意义。针对中文领域的隐喻识别任务, 我们提出了一种基于句法感知图卷积神经网络和ELECTRA的隐喻识别模型(Syntax-aware GCN withELECTRA SaGE)。该模型从语言学出发, 使用ELECTRA和Transformer编码器抽取句子的语义特征, 将句子按照依存关系组织成一张图并使用图卷积神经网络抽取其句法特征, 在此基础上对两类特征进行融合以进行隐喻识别。我们的模型在CCL2018中文隐喻识别评测数据集上以85. 22%的宏平均F1分数超越了此前的最佳成绩, 验证了融合语义信息和句法信息对于隐喻识别任务具有重要作用。”
用计量风格学方法考察《水浒传》的作者争议问题——以罗贯中《平妖传》为参照(Quantitive Stylistics Based Research on the Controversy of the Author of “Tales of the Marshes”: Comparing with “Pingyaozhuan” of Luo Guanzhong)
《水浒传》是独著还是合著, 施耐庵和罗贯中是何关系一直存在争议。本文将其作者争议粗略归纳为施耐庵作、罗贯中作、施作罗续、罗作他续、施作罗改五种情况, 以罗贯中的《平妖传》为参照, 用假设检验、文本聚类、文本分类、波动风格计量等方法, 结合对文本内容的分析, 考察《水浒传》的写作风格, 试图为其作者身份认定提供参考。结果显示, 只有罗作他续的可能性大, 即前70回为罗贯中所作, 后由他人续写, 其他四种情况可能性都较小。
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results.
Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Agile gesture recognition for low-power applications: customisation for generalisation
Automated hand gesture recognition has long been a focal point in the AI community.
Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics
Large language models have achieved remarkable success in general language understanding tasks.
YAYI 2: Multilingual Open-Source Large Language Models
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence.
Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series
Score-based diffusion method(i. e., CSDI) is effective for the time series imputation task but computationally expensive due to the nature of the generative diffusion model framework.
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering.
The Impact of Background Removal on Performance of Neural Networks for Fashion Image Classification and Segmentation
Fashion understanding is a hot topic in computer vision, with many applications having great business value in the market.
AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention and Text Attributes
We have tested and evaluated our AICAB NET on the MS COCO dataset and a new proposed Fashion dataset.
Adversarial Multi-task Learning for End-to-end Metaphor Detection
We leverage adversarial training to align the data distributions of MD and BSD in the same feature space, so task-invariant representations can be learned.
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation
Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding.
Agile gesture recognition for capacitive sensing devices: adapting on-the-job
However, there is growing demand for gesture recognition technology that can be implemented on low-power devices using limited sensor data instead of high-dimensional inputs like hand images.
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE
Though the identifiability is appealing, we show that iVAEs could have local minimum solution where observations and the approximated ICs are independent given covariates.-a phenomenon we referred to as the posterior collapse problem of iVAEs.
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
CSSR: A Context-Aware Sequential Software Service Recommendation Model
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub.
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
Visual-Semantic Transformer for Scene Text Recognition
In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (VST).
Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent Generative Adversarial Network
The lung volume volume was firstly delineated using a pre-trained U-net and worked as the input for the later network.
Physically Explainable CNN for SAR Image Classification
In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL).
Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network
Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health.
A Large-Scale Benchmark for Food Image Segmentation
Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks -- the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e. g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.
Ranked #3 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
Coexistience of phononic six-fold, four-fold and three-fold excitations in ternary antimonide Zr3Ni3Sb4
However, these materials are uncommon because these excitations in electronic systems are usually broken by spin-orbit coupling (SOC) and normally far from the Fermi level.
Materials Science
Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks
Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.
Physics and Society
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
A Knowledge Graph based Approach for Mobile Application Recommendation
With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders.
Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders
This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders.
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation
Although Generative Adversarial Networks have shown remarkable performance in image generation, there are some challenges in image realism and convergence speed.
Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk
A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue.
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision
This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot.
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
With the rapid development of online advertising and recommendation systems, click-through rate prediction is expected to play an increasingly important role. Recently many DNN-based models which follow a similar Embedding&MLP paradigm have been proposed, and have achieved good result in image/voice and nlp fields.
Multi-view Point Cloud Registration with Adaptive Convergence Threshold and its Application on 3D Model Retrieval
In this paper, we propose a framework to reconstruct the 3D models by the multi-view point cloud registration algorithm with adaptive convergence threshold, and subsequently apply it to 3D model retrieval.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification.
Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR.
Methodology
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks
One of the big restrictions in brain computer interface field is the very limited training samples, it is difficult to build a reliable and usable system with such limited data.
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection
In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
Sentiment Analysis by Capsules
Based on capsule representation, the probability module computes the capsule’s state probability.
Ranked #6 on
Sentiment Analysis
on MR
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data.
Three IQs of AI Systems and their Testing Methods
The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products.
Intelligence Quotient and Intelligence Grade of Artificial Intelligence
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy.
Two-dimensional Spin-Orbit Dirac Point in Monolayer HfGeTe
However, the Dirac points in existing 2D materials, including graphene, are vulnerable against spin-orbit coupling (SOC).
Materials Science
Image Type Water Meter Character Recognition Based on Embedded DSP
In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition.
Learning Gaussian Graphical Models with Observed or Latent FVSs
Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes.
