2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)最新文献_第4页

Efficient Quantization Techniques for Deep Neural Networks 深度神经网络的有效量化技术

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00059

Chutian Jiang

{"title":"Efficient Quantization Techniques for Deep Neural Networks","authors":"Chutian Jiang","doi":"10.1109/CONF-SPML54095.2021.00059","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00059","url":null,"abstract":"As model prediction becomes more and more accurate and the network becomes deeper and deeper, the amount of memory consumed by the neural network becomes a problem, especially on mobile devices. It is also very difficult to balance the tradeoff between computational cost and battery life, which makes mobile devices very hard as well to become smarter. Model quantification techniques provide the opportunity to tackle this tradeoff by reducing the memory bandwidth and storage and improving the system throughput and latency. This paper discusses and compares the state-of-the-art methods of neural network quantification methodologies including Post Training Quantization (PTQ) and Quantization Aware Training (QAT). PTQ directly quantizes the trained floating-point model. The implementation process is simple and does not require quantization during the training phase. QAT requires us to use simulated quantization operations to model the effect of the quantization, and forward and backward passes are usually performed in the floating-point model. Finally, as discussed in the experiments in this paper, we conclude that with the evolution of the quantization techniques, the accuracy gap between PTQ and QAT is shrinking.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134005528","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

A Training Method For VideoPose3D with Ideology of Action Recognition 基于动作识别思想的VideoPose3D训练方法

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00041

Hao Bai

引用次数: 0

Using Q-Learning to Personalize Pedagogical Policies for Addition Problems 运用Q-Learning实现加法问题教学策略的个性化

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00043

Danyating Shen, Takara E. Truong, C. Weintz

{"title":"Using Q-Learning to Personalize Pedagogical Policies for Addition Problems","authors":"Danyating Shen, Takara E. Truong, C. Weintz","doi":"10.1109/CONF-SPML54095.2021.00043","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00043","url":null,"abstract":"The prevalence of COVID-19 has illuminated the need for practical digital education tools over the past year. With students studying from home, teachers have struggled to provide their students with adequately challenging coursework. Our project aims to solve this issue in the context of math. More specifically, our goal is to encourage thoughtful learning by supplying students with personalized two-number addition problems that take time to solve but expect the student to answer correctly still. Our solution is to model the process of selecting a math problem to give a student as a Markov Decision Process (MDP) and then use Q-learning to determine the best policy for arriving at the most optimally challenging two-number addition problem for that student. The project creates three student simulators based on group member data. We show that it took student one: $(162 pm 134)$ iterations to give appropriate level problems where the first entry is mean and the second is the standard deviation. Student two took $(230 pm 205)$ iterations, and student three took $(247 pm 236)$ iterations. Lastly, we demonstrate that pre-training our model on students two and three and testing on student one showed a significant improvement from $(162 pm 134)$ iterations to $(35 pm 44)$ iterations.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123923812","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

Application of Machine Learning Algorithms in Speech Emotion Recognition 机器学习算法在语音情感识别中的应用

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00031

Junyi Cao

引用次数: 0

Integral Sliding Mode Trajectory Tracking Method for 1-DOP Manipulator Systems driven by Pneumatic Muscles on the Basis of the Nonlinear Extended State Observer 基于非线性扩展状态观测器的气动肌肉驱动1-DOP机械臂系统积分滑模轨迹跟踪方法

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00072

Yixin Liu

引用次数: 0

An Overview of Recommender Systems and Its Next Generation: Context-Aware Recommender Systems 推荐系统及其下一代概述:上下文感知推荐系统

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00015

Jiahao Liang

{"title":"An Overview of Recommender Systems and Its Next Generation: Context-Aware Recommender Systems","authors":"Jiahao Liang","doi":"10.1109/CONF-SPML54095.2021.00015","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00015","url":null,"abstract":"Recommender Systems have been generally utilized in different areas including motion pictures, news, music with an intend to give the most important recommendations to clients from an assortment of accessible alternatives. Recommender Systems are planned utilizing procedures from numerous fields, some of which are: AI, data recovery, information mining, direct variable based math and man-made consciousness. However, in typical commodity applications, due to the huge user and project library and just few evaluations (Sparsity issue), and at the point when the client is new to Recommender Frameworks, the framework can’t prescribe things that are applicable to clients in light of absence of past data about the client as well as the client thing rating history that assists with deciding the clients’ preferences (cold start). What’s more, presently there’s an innovation called Context-aware Recommender Systems (CARS), which utilizing setting information (location, time, peer, etc.) during the time spent proposal. In this work, we present an outline of some of noticeable customary RS and the high level CARS. We discuss the advantages and disadvantages of them. Furthermore, we reveal some inherent problems in RS. At last, we make a conclusion and give some challenges in current works.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115674781","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: 2

Mechatronics: System Analysis Based on Software Simulation and Programming 机电一体化:基于软件仿真与编程的系统分析

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00022

Xuanang Chen

引用次数: 0

Research on Neural Network Based Image and Video Denoising 基于神经网络的图像和视频去噪研究

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00023

Z. Luo

引用次数: 0

Comparative Study of the Optimization of the Multi-prime RSA Algorithm 多素数RSA算法优化的比较研究

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00039

Ziyuan Ma

引用次数: 0

Learning Unsupervised Side Information for Zero-Shot Learning 零射击学习的无监督侧信息学习

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00070

Fan Zhang

引用次数: 0