2021 13th International Conference on Machine Learning and Computing最新文献_第8页

An Unsupervised Feature Learning Method for Enhancing the Generalization of Cancer Diagnosis 一种增强癌症诊断泛化的无监督特征学习方法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457720

Zhen Liu, Ruoyu Wang, Wen-bo Zhang, Deyu Tang

引用次数: 1

K-CSRL: Knowledge Enhanced Conversational Semantic Role Labeling K-CSRL:知识增强会话语义角色标注

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457763

Boyu He, Han Wu, Congduan Li, Linqi Song, Weigang Chen

引用次数: 3

An Extended Factorial Hidden Markov Model for Non-Intrusive Load Monitoring Based on Density Peak Clustering 基于密度峰值聚类的非侵入式负荷监测扩展阶乘隐马尔可夫模型

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457712

Zhao Wu, Chao Wang, Ruiyou Li, Huaiqing Zhang

引用次数: 0

Using the Naive Bayes as a discriminative model 使用朴素贝叶斯作为判别模型

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457697

E. Azeraf, E. Monfrini, W. Pieczynski

{"title":"Using the Naive Bayes as a discriminative model","authors":"E. Azeraf, E. Monfrini, W. Pieczynski","doi":"10.1145/3457682.3457697","DOIUrl":"https://doi.org/10.1145/3457682.3457697","url":null,"abstract":"For classification tasks, probabilistic graphical models are usually categorized into two disjoint classes: generative or discriminative. It depends on the posterior probability p(x|y) of the label x given the observation y computation. On the one hand, generative models, like the Naive Bayes or the Hidden Markov Model (HMM), need the computation of the joint probability p(x, y), before using the Bayes rule to compute p(x|y). On the other hand, discriminative models compute p(x|y) directly, regardless of the observations’ law. They are intensively used nowadays, with models as Logistic Regression or Conditional Random Fields (CRF). However, the recent Entropic Forward-Backward algorithm shows that the HMM, considered as a generative model, can also match the discriminative one’s definition. This example leads to question if it is the case for other generative models. In this paper, we show that the Naive Bayes can also match the discriminative model definition, so it can be used in either a generative or a discriminative way. Moreover, this observation also discusses the notion of Generative-Discriminative pairs, linking, for example, Naive Bayes and Logistic Regression, or HMM and CRF. Related to this point, we show that the Logistic Regression can be viewed as a particular case of the Naive Bayes used in a discriminative way.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129468671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Spectral-wise Attention-based Residual Network for Hyperspectral Image Classification 基于光谱的残差网络用于高光谱图像分类

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457735

Yaxin Chen, Zhiqiang Guo, Jie Yang

引用次数: 0

Identifying the Key Residues Regulating the Binding between Antibody Avelumab and PD-L1 VIA Molecular Dynamics Simulation 通过分子动力学模拟鉴定调节抗体Avelumab与PD-L1结合的关键残基

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457767

Wenping Liu, Ting Chen, Shengsheng Lai, Gangping Zhang, Guangjian Liu, Haoyu Jin

{"title":"Identifying the Key Residues Regulating the Binding between Antibody Avelumab and PD-L1 VIA Molecular Dynamics Simulation","authors":"Wenping Liu, Ting Chen, Shengsheng Lai, Gangping Zhang, Guangjian Liu, Haoyu Jin","doi":"10.1145/3457682.3457767","DOIUrl":"https://doi.org/10.1145/3457682.3457767","url":null,"abstract":"Avelumab, approved by the US Food and Drug Administration (FDA) for the treatment of Merkel cell carcinoma in adults and paediatric patients in 2017, is an investigational fully human anti–PD-L1 IgG1 antibody that inhibits PD-1/PD-L1 interactions. Although the crystal structure of the avelumab/PD-L1 complex was reported in 2017, which provided us the interface information at atom level, the dynamics information of the complex is missed, and some key residues could not be detected in that static crystal structure. Here, molecular dynamics simulations were performed for the avelumab/PD-L1 complex to map the epitope to paratope residues. The results showed that the epitope residues locating on the C strand (PD-L1TYR56 and PD-L1GLU58), CC’ loop (PD-L1GLU60, PD-L1ASP61 and PD-L1LYS62), C’ strand (PD-L1ASN63), and C'D loop (PD-L1HIS69) of PD-L1 mainly form the interface with avelumab. The paratope residues on avelumab include TYR52H, SER54H, GLY102H, THR105H, TYR34L, ASP52L and ARG99L. The C’ strand of PD-L1 is also a binding region for PD-1. Thus, antibody avelumab block PD-1/PD-L1 interaction through direct competitive binding of the C’ strand of PD-L1.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126837932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

YOLO-Tight: an Efficient Dynamic Compression Method for YOLO Object Detection Networks YOLO- tight:一种用于YOLO目标检测网络的高效动态压缩方法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457740

Wei Yan, Ting Liu, Yuzhuo Fu

引用次数: 2

Multi-Perspective Reasoning Transformers 多角度推理变压器

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457759

Dagmawi Alemu Moges, Andre Niyongabo Rubungo, Hong Qu

{"title":"Multi-Perspective Reasoning Transformers","authors":"Dagmawi Alemu Moges, Andre Niyongabo Rubungo, Hong Qu","doi":"10.1145/3457682.3457759","DOIUrl":"https://doi.org/10.1145/3457682.3457759","url":null,"abstract":"Machine Reading Comprehension is defined as the ability of machines to read and understand unstructured text and answer questions about it. It is considered as a challenging task with wide range of enterprise applications. Wide range of natural language understanding and reasoning tasks are found embedded within machine reading comprehension datasets. This requires effective models with robust relational reasoning capabilities to answer complex questions. Reasoning in natural language is a long-term machine-learning goal and is critically needed for building intelligent agents. However, most papers heavily depend on underlying language modeling and thus pay little to no attention on creating effective reasoning models. This paper proposes a modified transformer architecture that effectively combines soft and hard attention to create multi-perspective reasoning model capable of tackling wide range of reasoning tasks. An attention mechanism that highlights the relational significance of input signals is considered as well. The result from this study shows performance gain as compared to its counterpart the transformer network on bAbI dataset, a natural language reasoning tasks.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129940334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DSP-PIGAN: A Precision-Consistency Machine Learning Algorithm for Solving Partial Differential Equations DSP-PIGAN:求解偏微分方程的精确一致性机器学习算法

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457686

Yunzhuo Wang, Hao Sun, Guangzhong Sun

引用次数: 2

Low Light Image Enhancement in USV Imaging System Via U-Net and Attention Mechanism 基于U-Net和注意机制的USV成像系统弱光图像增强

2021 13th International Conference on Machine Learning and Computing Pub Date : 2021-02-26 DOI: 10.1145/3457682.3457729

Sheng Zhang, Tianxiao Cai, Yihang Chen

{"title":"Low Light Image Enhancement in USV Imaging System Via U-Net and Attention Mechanism","authors":"Sheng Zhang, Tianxiao Cai, Yihang Chen","doi":"10.1145/3457682.3457729","DOIUrl":"https://doi.org/10.1145/3457682.3457729","url":null,"abstract":"Images captured by Unmanned Surface Vessel (USV) have a wide range of applications in various fields, such as maritime object detection, remote sensing, and autonomous transportation. However, cameras often suffer from a low light environment, resulting in low contrast, high noise, and poor quality image, causing identification difficulties and machine decision errors. In recent years, convolutional neural networks have developed rapidly, which have strong generalization ability and can extract different levels of information, especially high-level information. Therefore, to preprocess low light images before advanced computer vision tasks of USV, we proposed a deep learning-based end-to-end convolutional network for low light enhancement in USV imaging system. The advantage of our model is using U-Net as the basic architecture to gain multi-scale feature maps with improvements, including attention mechanism and dense connection. Besides, we pay attention to edge information given images' edge loss. With the unique network structure, our model can effectively increase the brightness and contrast of dark aquatic images. Experiments have been carried out on testing images to analyze our proposed method with several latest imaging methods. The experimental results show its outstanding performance in both subjective and objective evaluation.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125186563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1