2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第9页

Size Does Matter: Overcoming Limitations during Training when using a Feature Pyramid Network 大小很重要:在使用特征金字塔网络时克服训练中的限制

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00249

Fabian Fallas-Moya, Manfred Gonzalez-Hernandez, Amir Sadovnik

{"title":"Size Does Matter: Overcoming Limitations during Training when using a Feature Pyramid Network","authors":"Fabian Fallas-Moya, Manfred Gonzalez-Hernandez, Amir Sadovnik","doi":"10.1109/ICMLA52953.2021.00249","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00249","url":null,"abstract":"State-of-the-art object detectors need to be trained with a wide variety of data in order to perform well in real-world problems. Training-data-diversity is very important to achieve good generalization. However, there are scenarios where we have training data with certain limitations. One such scenario is when the objects of the testing set have a different size (discrepancy) from the objects used during training. Another scenario is when we have high-resolution images with a dimension that is not supported by the model. To address these problems, we propose a novel pipeline that is able to handle high-resolution images by cropping the original image into sub-images and put them back in the end. Also, in the case of the discrepancy of object sizes, we propose two different techniques based on scaling the image up and down in order to have an acceptable performance. In addition, we also use the information from the Feature Pyramid Network to remove false-positives. Our proposed methods overcome state-of-the-art data augmentation policies and our models can generalize to different object sizes even though limited data is provided.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"42 1","pages":"1553-1560"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75456976","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

Financial Time Series Forecasting Enriched with Textual Information 丰富文本信息的金融时间序列预测

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00066

Lord Flaubert Steve Ataucuri Cruz, D. F. Silva

引用次数: 1

Retrieval Enhanced Ensemble Model Framework For Rumor Detection On Micro-blogging Platforms 微博平台谣言检测的检索增强集成模型框架

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00042

Rishab Sharma, F. H. Fard, Apurva Narayan

引用次数: 1

A Transformer-based Approach for Translating Natural Language to Bash Commands 将自然语言转换为Bash命令的基于转换器的方法

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00202

Quchen Fu, Zhongwei Teng, Jules White, D. Schmidt

引用次数: 8

On Learning Probabilistic Partial Lexicographic Preference Trees 学习概率部分字典偏好树

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00051

Xudong Liu

引用次数: 0

Using Generative Adversarial Networks and Non-Roadside Video Data to Generate Pedestrian Crossing Scenarios 使用生成对抗网络和非路边视频数据生成行人过街场景

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00228

James Spooner, V. Palade, A. Daneshkhah, S. Kanarachos

{"title":"Using Generative Adversarial Networks and Non-Roadside Video Data to Generate Pedestrian Crossing Scenarios","authors":"James Spooner, V. Palade, A. Daneshkhah, S. Kanarachos","doi":"10.1109/ICMLA52953.2021.00228","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00228","url":null,"abstract":"As fully autonomous driving is introduced on our roads, the safety of vulnerable road users is of the greatest importance. Available real-world data is limited and often lacks the variety required to ensure the safe deployment of new technologies. This paper builds on a novel generation method to generate pedestrian crossing scenarios for autonomous vehicle testing, known as the Ped-Cross GAN. While our previously developed Pedestrian Scenario dataset [1] is extremely detailed, there exist labels in the dataset where available data is severely imbalanced. In this paper, augmented non-roadside data is used to improve the generation results of pedestrians running at the roadside, increasing the classification accuracy from 20.95% to 82.56%, by increasing the training data by only 30%. This proves that researchers can generate rare, edge case scenarios using the Ped-Cross GAN, by successfully supplementing available data with additional non-roadside data. This will allow for adequate testing and greater test coverage when testing the performance of autonomous vehicles in pedestrian crossing scenarios. Ultimately, this will lead to fewer pedestrian casualties on our roads.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"59 1","pages":"1413-1420"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75964565","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

ConfusionTree-Pattern: A Hierarchical Design for an Efficient and Performant Multi-Class Pattern 混淆树模式:一种高效、高性能的多类模式的分层设计

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00125

M. F. Adesso, Nicola Wolpert, E. Schömer

引用次数: 0

Homology Preserving Graph Compression 保持同调的图压缩

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00153

M. E. Aktas, Thu Nguyen, Esra Akbas

引用次数: 1

Emotion Recognition and Sentiment Classification using BERT with Data Augmentation and Emotion Lexicon Enrichment 基于BERT的情感识别和情感分类

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00037

Vishwa Sai Kodiyala, Robert E. Mercer

引用次数: 2

Incremental Learning Vector Auto Regression for Forecasting with Edge Devices 基于边缘设备的增量学习向量自回归预测

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2021-12-01 DOI: 10.1109/ICMLA52953.2021.00188

Venkata Pesala, T. Paul, Ken Ueno, H. P. Bugata, Ankit Kesarwani

{"title":"Incremental Learning Vector Auto Regression for Forecasting with Edge Devices","authors":"Venkata Pesala, T. Paul, Ken Ueno, H. P. Bugata, Ankit Kesarwani","doi":"10.1109/ICMLA52953.2021.00188","DOIUrl":"https://doi.org/10.1109/ICMLA52953.2021.00188","url":null,"abstract":"It is common to forecast time-series data in a cloud server environment by building a forecasting model after collecting all the time-series data at the server-side. However, this may not be efficient in time-critical forecasting, control, and decision-making due to high latency, bandwidth, and network connectivity issues. Hence, edge devices can be employed to make quick forecasting on a real-time basis. However, due to limited computing resources and processing power, edge devices cannot handle a huge volume of multivariate time-series data. Therefore, it is desirable to develop an algorithm that trains and updates a forecasting model incrementally. This can be done by using a small chunk of multivariate time-series data without sacrificing the forecasting accuracy, while training and inference can be executed in the edge device itself. In this context, we propose a new forecasting method called Incremental Learning Vector Auto Regression (ILVAR). It works by minimizing the variance difference between actual and forecasted values as a new chunk of time-series data arrives sequentially and thereby it updates the forecasting model incrementally. To show the effectiveness of the proposed method, experiments were performed on 11 publicly available datasets from diverse domains using Raspberry Pi-2 as an edge device and evaluated using five metrics such as MAPE, RMSE, $mathrm{R}^{2}$ score, Computational time, and Memory consumption for 1-step and 24-step ahead forecasting tasks. The performance was compared with the state-of-the-art methods such as Vector Auto Regression (VAR), Incremental Learning Extreme Learning Machine (ILELM), and Incremental Learning Long Short-Term Memory (ILLSTM). These experimental results suggest that our proposed method performs better than existing methods and is able to achieve the desired performance for forecasting with edge devices.","PeriodicalId":6750,"journal":{"name":"2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"185 1","pages":"1153-1159"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85455649","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: 4