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Disentangling and learning robust representations with natural clustering 18

Disentangling and learning robust representations with natur...

引用

18th IEEE international conference on machine learning and applications, icmla 2019

作者： Antoran, Javier Miguel, Antonio University of Zaragoza Zaragoza Spain

ISBN: (纸本)9781728145495

learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. this model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data. © 2019 IEEE.

关键词： learning systems

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Robust modeling of continuous 4-D affective space from EEG recording 15

Robust modeling of continuous 4-D affective space from EEG r...

引用

15th IEEE international conference on machine learning and applications (icmla)

作者： Al-Fahad, Rakib Yeasin, Mohammed Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

ISBN: (纸本)9781509061679

the inherent intangible nature, complexity, context-specific interpretations of emotions make it difficult to quantify and model affective space. Dimensional theory is one of the effective methods to describe and model emotions. Despite recent advances in affective computing, modeling continuous affective space remains a challenge. Here, we present a computational framework to study the role of functional areas of brain and band frequencies in modeling 4-D continuous affective space (Valence, Arousal, Like and Dominance). In particular, we used Electroencephalogram (EEG) recordings and adopted a recursive feature elimination (RFE) approach to select band frequencies and electrode locations (functional areas) that are most relevant for predicting affective space. Empirical analyses on DEAP dataset [1] reveals that only a small number of locations (7-12) and certain band frequencies carry most discriminative information. Using the selected features, we modeled 4-D affective space using Support Vector Regression (SVR). Regression analysis show that Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, Like are 1.40, 1.23, 1.24 and, 1.24, respectively. Besides SVR, the performance of feature fusion and ensemble classifiers were also compared to determine the robust model against technical noise and individual variations. It was observed that the prediction accuracy of the final model is up to 37% better than human judgment evaluated on same data set. Spillover effect of our approach may include design of task-specific (i.e., emotion, memory capacity) EEG headset with a minimal number of electrodes.

关键词： Affective space EEG feature selection robust modeling functional area of brain assistive technology

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BP-Net: Efficient Deep learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram 20

BP-Net: Efficient Deep Learning for Continuous Arterial Bloo...

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20th IEEE international conference on machine learning and applications (icmla)

作者： Vardhan, Rishi K. Vedanth, S. Poojah, G. Abhishek, K. Kumar, Nitish M. Vijayaraghavan, Vineeth SSN Coll Engn Chennai Tamil Nadu India Coll Engn Chennai Tamil Nadu India Solarill Fdn Chennai Tamil Nadu India

ISBN: (纸本)9781665443371

Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke;therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current tailless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photo-plethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform.

关键词： Blood Pressure Photoplethysmogram Arterial Blood Pressure U-Net wearable biomedical applications non-invasive

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Deep learning based Time Series Forecasting 19

Deep Learning based Time Series Forecasting

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19th IEEE international conference on machine learning and applications, icmla 2020

作者： Agarwal, Kushagra Dheekollu, Lalasa Dhama, Gaurav Arora, Ankur Asthana, Siddhartha Bhowmik, Tanmoy ai Garage Mastercard Gurgaon India

ISBN: (纸本)9781728184708

For decision-makers in the forecasting sector, decision processes like planning of facilities, an optimal day-to-day operation within the domain etc., are complex with several different levels to be considered. these decisions address widely different time horizons and aspects of the system, making it difficult to model. the advent of deep learning in forecasting solved the need for expensive hand-crafted features and deep domain knowledge. the work aims at giving a structure to the existing literature for time-series forecasting in deep learning. Based on the underlying structures of the technique, such as RNN, CNN, and Transformer, we have categorized various deep learning-based time series forecasting techniques and provided a consolidated report. Additionally, we have performed experiments to compare these techniques on 4 different publicly available datasets. Finally, based on these experiments, we provide an intuitive reasoning behind these performances. We believe that this work shall help the researchers in choosing relevant techniques for future research. © 2020 IEEE.

关键词： Time series

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A Combination of Positive and Negative Fuzzy Rules for Image Classification Problem

A Combination of Positive and Negative Fuzzy Rules for Image...

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7th international conference on machine learning and applications

作者： Nguyen, thanh Minh Wu, Q. M. Jonathan Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9780769534954

In this paper we propose a new fuzzy rule-based system for application in image classification problem. Each rule in our proposed system can represent more than one class. While traditional fuzzy systems consider positive fuzzy rules only, in this paper, we focus on combining negative fuzzy rules with traditional positive ones leading to fuzzy inference systems. this new approach has been tested on image classification problem consisting of multiple images with excellent results.

关键词： Fuzzy rules

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Fuzzy-ExCOM Software Project Risk Assessment

Fuzzy-ExCOM Software Project Risk Assessment

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11th IEEE international conference on machine learning and applications (icmla)

作者： Manalif, Ekananta Capretz, Luiz Fernando Nassif, Ali Bou Ho, Danny Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada NFA Estimat Inc Richmond Hill ON L4C 0A2 Canada

ISBN: (纸本)9780769549132

A software development project is considered to be risky due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risks in new software projects. However, the original method has limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very Low (VL), Low (L), Nominal (N), High (H), Very High (VH) and Extra High (XII). this paper introduces the fuzzy-ExCOM methodology that combines the advantages of a fuzzy technique with Expert COCOMO methodology for risk assessment in software projects. the validation of this approach with industrial data shows that fuzzy-ExCOM provides better risk assessment results with a higher level of sensitivity with respect to risk identification compared to the original Expert COCOMO methodology.

关键词： software project fuzzy technique risk assessment

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A study of smoothing algorithms for item categorization on e-commerce sites

A study of smoothing algorithms for item categorization on e...

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9th international conference on machine learning and applications, icmla 2010

作者： Shen, Dan Ruvini, Jean-David Mukherjee, Rajyashree Sundaresan, Neel eBay Research Labs. No. 88 KeYuan Rd Shanghai China eBay Research Labs. 2005 E Hamilton Ave San Jose CA 95125 United States

ISBN: (纸本)9780769543000

One central issue in a long-tail online marketplace such as eBay is to automatically put user self-input items into a catalog in real time. this task is extremely challenging when the inventory scales up, the items become ephemeral, and the user input remains noisy. Indeed, catalog learning has emerged as a key technical property for other major online ecommerce applications including search and recommendation. We formulate the item cataloging task as a Bayesian classification problem, which shall scale well in very large data set and have good online prediction performance. the inherent data sparseness issue, especially for those tail categories, is key to the overall model performance. We address the data sparseness issue by adapting statistically sound smoothing methods well studied in language modeling tasks. However, there are data characteristics specific to the ecommerce domain, including short yet focused item description, very large and hierarchical catalog taxonomy, and highly skewed distribution over types of items. We investigate these domain-specific regularities empirically, and report practically significant results with realworld true-scale data. © 2010 IEEE.

关键词： Modeling languages

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An industry case of large-scale demand forecasting of hierarchical components 18

An industry case of large-scale demand forecasting of hierar...

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18th IEEE international conference on machine learning and applications, icmla 2019

作者： Rivera-Castro, Rodrigo Nazarov, Ivan Xiang, Yuke Maksimov, Ivan Pletnev, Aleksandr Burnaev, Evgeny Skolkovo Institute of Science and Technology Russia Huawei Noah's Ark Lab

ISBN: (纸本)9781728145495

Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts remain pervasive in the industry. Demand planners require easy-to-understand tools capable of delivering state-of-the-art results. this work presents an industry case of demand forecasting at one of the largest manufacturers of electronics in the world. It seeks to support practitioners with five contributions: (1) A benchmark of fourteen demand forecast methods applied to a relevant data set, (2) A data transformation technique yielding comparable results with state of the art, (3) An alternative to ARIMA based on matrix factorization, (4) A model selection technique based on topological data analysis for time series and (5) A novel data set. Organizations seeking to up-skill existing personnel and increase forecast accuracy will find value in this work. © 2019 IEEE.

关键词： machine learning

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machine learning for seismic signal processing: Seismic phase classification on a manifold

Machine learning for seismic signal processing: Seismic phas...

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10th international conference on machine learning and applications, icmla 2011

作者： Ramirez Jr., Juan Meyer, Francois G. Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 United States

ISBN: (纸本)9780769546070

In this research, we consider the supervised learning problem of seismic phase classification. In seismology, knowledge of the seismic activity arrival time and phase leads to epicenter localization and surface velocity estimates useful in developing seismic early warning systems and detecting man-made seismic events. Formally, the activity arrival time refers to the moment at which a seismic wave is first detected and the seismic phase classifies the physics of the wave. We propose a new perspective for the classification of seismic phases in three-channel seismic data collected within a network of regional recording stations. Our method extends current techniques and incorporates concepts from machine learning. machine learning techniques attempt to leverage the concept of "learning" the patterns associated with different types of data characteristics. In this case, the data characteristics are the seismic phases. this concept makes sense because the characteristics of the phase types are dictated by the physics of wave propagation. thus by "learning" a signature for each type of phase, we can apply classification algorithms to identify the phase of incoming data from a database of known phases observed over the recording network. Our method first uses a multi-scale feature extraction technique for clustering seismic data on low-dimensional manifolds. We then apply kernel ridge regression on each feature manifold for phase classification. In addition, we have designed an information theoretic measure used to merge regression scores across the multi-scale feature manifolds. Our approach complements current methods in seismic phase classification and brings to light machine learning techniques not yet fully examined in the context of seismology. We have applied our technique to a seismic data set from the Idaho, Montana, Wyoming, and Utah regions collected during 2005 and 2006. this data set contained compression wave and surface wave seismic phases. through cross-v

关键词： Seismic waves

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Partly Supervised Multi-Task learning 19

Partly Supervised Multi-Task Learning

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19th IEEE international conference on machine learning and applications, icmla 2020

作者： Imran, Abdullah-Al-Zubaer Huang, Chao Tang, Hui Fan, Wei Xiao, Yuan Hao, Dingjun Qian, Zhen Terzopoulos, Demetri University of California Los AngelesCA United States Tencent Hippocrates Research Lab Palo AltoCA United States Xi'an Jiaotong University College of Medicine Xi'an China

ISBN: (纸本)9781728184708

Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model for multiple tasks can provide better generalizability than single-task learning. Leveraging self-supervision and adversarial training, we propose a novel, general purpose semi-supervised, multiple-task model - namely, self-supervised, semi-supervised, multi-task learning (S4MTL) - for accomplishing two important medical image analysis tasks: segmentation and diagnostic classification. Experimental results on chest and spine X-ray datasets confirm that our S4MTL model significantly outperforms semi-supervised single-task, semi/fully-supervised multi-task, and fully-supervised single-task models, even with a 50% reduction in class and segmentation labels. © 2020 IEEE.

关键词： Image segmentation

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