scholarly journals Reducing Training Time in a One-Shot Machine Learning-Based Compiler

2010 ◽  
pp. 399-407 ◽  
Author(s):  
John Thomson ◽  
Michael O’Boyle ◽  
Grigori Fursin ◽  
Björn Franke
Keyword(s):  
Machine Learning ◽  
Training Time

scholarly journals Federated Quantum Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 460
Author(s):  
Samuel Yen-Chi Chen ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Research Direction ◽  
Future Research ◽  
Quantum Computers ◽  
Training Time ◽  
Quantum Neural Network ◽  
Distributed Training ◽  
Machine Learning Model ◽  
Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

Boosting the Training Time of Weakly Coordinated Distributed Machine Learning

2021 ◽  
Author(s):  
Erika Duriakova ◽  
Elias Tragos ◽  
Aonghus Lawlor ◽  
Barry Smyth ◽  
Neil Hurley
Keyword(s):  
Machine Learning ◽  
Training Time ◽  
Distributed Machine Learning

scholarly journals Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Gautam Pal ◽  
Xianbin Hong ◽  
Zhuo Wang ◽  
Hongyi Wu ◽  
Gangmin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lifelong Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Training Time ◽  
Root Cause ◽  
Random Decision Forest ◽  
Batch Data ◽  
Over Time ◽  
Decision Forest

Abstract Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute’s Genomic Data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each patient. Discussion and evaluation The model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov–Smirnov test. We present clustering and Random Decision Forest results. The model’s prediction accuracy is compared with standard machine learning algorithms for numeric and categorical fields. Conclusion We propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolate anomalous patient data and provides reasoning through root cause analysis by feature correlations with an aim to improve the overall survival rate. While the stream clustering technique creates groups of patient profiles, RDF further drills down into each group for comparison and reasoning for useful actionable insights. The proposed MALA architecture retains the past learned knowledge and transfer to future learning and iteratively becomes more knowledgeable over time.

scholarly journals Attacking Data Transforming Learners at Training Time

2019 ◽  
Vol 33 ◽  
pp. 3167-3174
Author(s):  
Scott Alfeld ◽  
Ara Vartanian ◽  
Lucas Newman-Johnson ◽  
Benjamin I.P. Rubinstein
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Ordinary Least Squares ◽  
General Purpose ◽  
Training Time ◽  
Target Values ◽  
Gradient Based ◽  
Deployment Time ◽  
Compositional Learning

While machine learning systems are known to be vulnerable to data-manipulation attacks at both training and deployment time, little is known about how to adapt attacks when the defender transforms data prior to model estimation. We consider the setting where the defender Bob first transforms the data then learns a model from the result; Alice, the attacker, perturbs Bob’s input data prior to him transforming it. We develop a general-purpose “plug and play” framework for gradient-based attacks based on matrix differentials, focusing on ordinary least-squares linear regression. This allows learning algorithms and data transformations to be paired and composed arbitrarily: attacks can be adapted through the use of the chain rule—analogous to backpropagation on neural network parameters—to compositional learning maps. Bestresponse attacks can be computed through matrix multiplications from a library of attack matrices for transformations and learners. Our treatment of linear regression extends state-ofthe-art attacks at training time, by permitting the attacker to affect both features and targets optimally and simultaneously. We explore several transformations broadly used across machine learning with a driving motivation for our work being autogressive modeling. There, Bob transforms a univariate time series into a matrix of observations and vector of target values which can then be fed into standard learners. Under this learning reduction, a perturbation from Alice to a single value of the time series affects features of several data points along with target values.

scholarly journals Learning similarity measures from data

2019 ◽  
Vol 9 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Agnar Aamodt ◽  
Kerstin Bach ◽  
Helge Langseth
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Learning System ◽  
Case Based Reasoning ◽  
Training Time ◽  
Domain Experts ◽  
Trained Classifier ◽  
Clustering Data

Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

A Comparison of Machine Learning Classifiers

2011 ◽  
Vol 271-273 ◽  
pp. 149-153 ◽  
Author(s):  
Phani Srikanth ◽  
Amarjot Singh ◽  
Devinder Kumar ◽  
Aditya Nagrare ◽  
Vivek Angoth
Keyword(s):  
Machine Learning ◽  
Time Analysis ◽  
Cancer Dataset ◽  
Classification Techniques ◽  
Training Time ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Precision And Accuracy

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

scholarly journals Machine Learning Algorithms for automated Image Capture and Identification of Fall Armyworm (FAW) Moths

2019 ◽  
Vol 3 (1) ◽  
pp. 1-4
Author(s):  
Simon Hawatichke Chiwamba ◽  
Jackson Phiri ◽  
Philip O. Y. Nkunika ◽  
Mayumbo Nyirenda ◽  
Monica M. Kabemba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Fall Armyworm ◽  
Machine Learning Algorithms ◽  
Training Time ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Learning Technique ◽  
Computer Scientists ◽  
Learning Software

Automated entomology is one of the field that has received a fair attention from the computer scientists and its support disciplines. This can further be confirmed by the recent attention that the Fall Armyworm (FAW) (Spodoptera frugiperda) has received in Africa particularly the Southern African Development Community (SADC). As the FAW is known for its devastating effects, stakeholders such as the Food and Agriculture Organization (FAO), SADC and University of Zambia (UNZA) have agreed to develop robust early monitoring and warning system. To supplement the stakeholders’ efforts, we choose a branch of artificial intelligence that employs deep neural network architectures known as Google TensorFlow. It is an advanced state-of-the-art machine learning technique that can be used to identify the FAW moths. In this paper, we use Google TensorFlow, an open source deep learning software library for defining, training and deploying machine learning models. We use the transfer learning technique to retrain the Inception v3 model in TensorFlow on the insect dataset, which reduces the training time and improve the accuracy of FAW moth identification. Our retrained model achieves a train accuracy of 57 – 60 %, cross entropy of 65 – 70% and validation accuracy of 

Handling Large Medical Data Sets for Disease Detection

2011 ◽  
pp. 162-176 ◽  
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Biomedical Engineering ◽  
Intelligent Systems ◽  
Computational Cost ◽  
Poor Performance ◽  
Medical Data ◽  
Data Sets ◽  
Training Time ◽  
Low Computational Cost

The breakthrough in the field of intelligent systems has spread its fruits to the field of biomedical engineering as well; where a series of models are being applied to automatically detect diseases based on some parameters or inputs. The continuous research in this field has resulted in a large amount of database being created for many diseases which becomes very difficult to train. Also the number of attributes is under constant rise. This increases the dimensionality of the problem and ultimately leads to poor performance. In this chapter we deal with the methods to handle these situations. We discuss the mechanism to divide data between different sub-systems. We also discuss the method of division of the attributes to reduce the training time and complexity. The resultant systems are able to train better due to low computational cost and hence give better performance. We validated this with the Breast Cancer database from the UCI Machine Learning repository and found our algorithm optimal.

Sign in / Sign up

Export Citation Format

Share Document