2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第2页

Globally Optimal Energy Efficiency Maximization for Capacity-Limited Fronthaul Crans with Dynamic Power Amplifiers’ Efficiency 基于动态功率放大器效率的有限容量前传起重机全局最优能效最大化

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-13 DOI: 10.1109/ICASSP.2018.8461308

K. Nguyen, Quang-Doanh Vu, Le-Nam Tran, M. Juntti

引用次数: 1

Orthogonality-Regularized Masked NMF for Learning on Weakly Labeled Audio Data 用于弱标记音频数据学习的正交正则化掩膜NMF

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-13 DOI: 10.1109/ICASSP.2018.8461293

I. Sobieraj, Lucas Rencker, Mark D. Plumbley

引用次数: 4

Individual Difference of Ultrasonic Transducers for Parametric Array Loudspeaker 参数阵列扬声器超声换能器的个体差异

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8462189

Shota Minami, Jun Kuroda, Yasuhiro Oikawa

引用次数: 2

Quickest Detection of Dynamic Events in Sensor Networks 传感器网络中动态事件的快速检测

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461854

Shaofeng Zou, V. Veeravalli

引用次数: 14

On Selecting Antenna Placements in Indoor Radio Environments 关于室内无线电环境中天线放置位置的选择

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8462374

Fabian Agren, Johan Sward, A. Jakobsson

引用次数: 0

A Penalized Method for the Predictive Limit of Learning 学习预测极限的惩罚方法

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461832

Jie Ding, Enmao Diao, Jiawei Zhou, V. Tarokh

{"title":"A Penalized Method for the Predictive Limit of Learning","authors":"Jie Ding, Enmao Diao, Jiawei Zhou, V. Tarokh","doi":"10.1109/ICASSP.2018.8461832","DOIUrl":"https://doi.org/10.1109/ICASSP.2018.8461832","url":null,"abstract":"Machine learning systems learn from and make predictions by building models from observed data. Because large models tend to overfit while small models tend to underfit for a given fixed dataset, a critical challenge is to select an appropriate model (e.g. set of variables/features). Model selection aims to strike a balance between the goodness of fit and model complexity, and thus to gain reliable predictive power. In this paper, we study a penalized model selection technique that asymptotically achieves the optimal expected prediction loss (referred to as the limit of learning) offered by a set of candidate models. We prove that the proposed procedure is both statistically efficient in the sense that it asymptotically approaches the limit of learning, and computationally efficient in the sense that it can be much faster than cross validation methods. Our theory applies for a wide variety of model classes, loss functions, and high dimensions (in the sense that the models' complexity can grow with data size). We released a python package with our proposed method for general usage like logistic regression and neural networks.","PeriodicalId":6638,"journal":{"name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"38 1","pages":"4414-4418"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86695862","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

Concurrent Clutter and Noise Suppression via Low Rank Plus Sparse Optimization for Non-Contrast Ultrasound Flow Doppler Processing in Microvasculature 基于低秩加稀疏优化的微血管非对比超声血流多普勒处理杂波和噪声抑制

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461638

Mahdi Bayat, M. Fatemi

引用次数: 18

Online Multi-Kernel Learning with Orthogonal Random Features 基于正交随机特征的在线多核学习

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461509

Yanning Shen, Tianyi Chen, G. Giannakis

引用次数: 6

An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks 一种确定深度神经网络最小逐层精度的解析方法

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461702

Charbel Sakr, Naresh R Shanbhag

{"title":"An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks","authors":"Charbel Sakr, Naresh R Shanbhag","doi":"10.1109/ICASSP.2018.8461702","DOIUrl":"https://doi.org/10.1109/ICASSP.2018.8461702","url":null,"abstract":"There has been growing interest in the deployment of deep learning systems onto resource-constrained platforms for fast and efficient inference. However, typical models are overwhelmingly complex, making such integration very challenging and requiring compression mechanisms such as reduced precision. We present a layer-wise granular precision analysis which allows us to efficiently quantize pre-trained deep neural networks at minimal cost in terms of accuracy degradation. Our results are consistent with recent findings that perturbations in earlier layers are most destructive and hence needing more precision than in later layers. Our approach allows for significant complexity reduction demonstrated by numerical results on the MNIST and CIFAR-10 datasets. Indeed, for an equivalent level of accuracy, our fine-grained approach reduces the minimum precision in the network up to 8 bits over a naive uniform assignment. Furthermore, we match the accuracy level of a state-of-the-art binary network while requiring up to ~ 3.5 × lower complexity. Similarly, when compared to a state-of-the-art fixed-point network, the complexity savings are even higher (up to ~ 14×) with no loss in accuracy.","PeriodicalId":6638,"journal":{"name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"124 1","pages":"1090-1094"},"PeriodicalIF":0.0,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88035638","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: 32

Harnessing Bandit Online Learning to Low-Latency Fog Computing 利用强盗在线学习低延迟雾计算

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2018-09-10 DOI: 10.1109/ICASSP.2018.8461641

Tianyi Chen, G. Giannakis

引用次数: 7