scholarly journals Large-Scale Machine Learning on Debugging Machine Learning Systems

2019 ◽  
pp. 355-360
Author(s):  
K. Ravikumar ◽  
M. Maheswaran
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Predictive Analytics ◽  
Learning Systems ◽  
Short Article ◽  
Information Mining ◽  
Investigative Process ◽  
Working Out ◽  
Selection Of

A computation indicated applying Tensor Movement may be accomplished with minimum modify on a wide selection of heterogeneous methods, including cellular devices such as for example devices and pills around large-scale spread methods of a huge selection of products and 1000s of computational units such as for example GPU cards. Even with arrangement, it's frequent to find out restrictions of the design or improvements in the goal notion that necessitate improvements to working out information and parameters. But, by nowadays, there's number frequent knowledge by what these iterations contain, or what debugging resources are required to help the investigative process. As more information becomes accessible, more formidable issues may be tackled. Consequently, device understanding is commonly utilized in pc technology and different fields. But, establishing effective device understanding programs involves an amazing level of "dark art" that's difficult to find in textbooks. This short article summarizes a dozen critical classes that device understanding scientists and practitioners have learned. These calculations are useful for numerous applications like information mining, picture running, predictive analytics, etc. to call a few. The key benefit of applying device understanding is that, when an algorithm finds what direction to go with information, it may do their function automatically.

scholarly journals Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

2019 ◽  
Author(s):  
WooSeok Jeong ◽  
Samuel J. Stoneburner ◽  
Daniel King ◽  
Ruye Li ◽  
Andrew Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Density Functional ◽  
Large Scale ◽  
Black Box ◽  
Target System ◽  
Complete Active Space ◽  
Active Space ◽  
Bond Dissociation ◽  
Selection Of

<div>Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.</div>

Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions

2020 ◽  
Vol 127 ◽  
pp. 106368 ◽  
Author(s):  
Lucy Ellen Lwakatare ◽  
Aiswarya Raj ◽  
Ivica Crnkovic ◽  
Jan Bosch ◽  
Helena Holmström Olsson
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Large Scale ◽  
Learning Systems ◽  
Industrial Settings

scholarly journals Event Classification with Multi-step Machine Learning

2021 ◽  
Vol 251 ◽  
pp. 03036
Author(s):  
Masahiko Saito ◽  
Tomoe Kishimoto ◽  
Yuya Kaneta ◽  
Taichi Itoh ◽  
Yoshiaki Umeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Systems ◽  
Loss Functions ◽  
Grid Search ◽  
Event Classification ◽  
Neural Architecture ◽  
End To End ◽  
Single Path ◽  
Large Model ◽  
Selection Of

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of an ML model from several small ML model candidates for each sub-task has been performed by using the idea based on Neural Architecture Search (NAS). In this paper, Differentiable Architecture Search (DARTS) and Single Path One-Shot NAS (SPOS-NAS) are tested, where the construction of loss functions is improved to keep all ML models smoothly learning. Using DARTS and SPOS-NAS as an optimization and selection as well as the connections for multi-step machine learning systems, we find that (1) such a system can quickly and successfully select highly performant model combinations, and (2) the selected models are consistent with baseline algorithms, such as grid search, and their outputs are well controlled.

scholarly journals The importance of ensuring artificial intelligence and machine learning can be understood at the human level

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
S Ram
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Black Box ◽  
User Engagement ◽  
Box Models ◽  
Black Box Models

Abstract With rapid developments in big data technology and the prevalence of large-scale datasets from diverse sources, the healthcare predictive analytics (HPA) field is witnessing a dramatic surge in interest. In healthcare, it is not only important to provide accurate predictions, but also critical to provide reliable explanations to the underlying black-box models making the predictions. Such explanations can play a crucial role in not only supporting clinical decision-making but also facilitating user engagement and patient safety. If users and decision makers do not have faith in the HPA model, it is highly likely that they will reject its use. Furthermore, it is extremely risky to blindly accept and apply the results derived from black-box models, which might lead to undesirable consequences or life-threatening outcomes in domains with high stakes such as healthcare. As machine learning and artificial intelligence systems are becoming more capable and ubiquitous, explainable artificial intelligence and machine learning interpretability are garnering significant attention among practitioners and researchers. The introduction of policies such as the General Data Protection Regulation (GDPR), has amplified the need for ensuring human interpretability of prediction models. In this talk I will discuss methods and applications for developing local as well as global explanations from machine learning and the value they can provide for healthcare prediction.

Software-Based Testing Kit Using Machine Learning for Diagnosis and Predictive Analytics of COVID-19 Patients

2021 ◽  
Vol 12 (2) ◽  
pp. 39-50
Author(s):  
Vishal Kumar Goar ◽  
Jyoti Prabha
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Prime Factor ◽  
Research Work ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm ◽  
Selection Of

Nowadays, the global community is being affected with COVID-19 disease and integrated infections, which are becoming a menace to the whole world. Research is going on to find out the solution, and still, no particular vaccination or solution has been achieved. This research work is focusing on the analytics of dataset extracted, which has assorted attributes, and these attributes are processed in the machine learning algorithm so that the prime factor can be recognized. In this research manuscript, the usage of COVID-19 dataset is done and trained using supervised learning approach of artificial neural network (ANN) on Levenberg-Marquardt (LM) algorithm so that the predictions of the test patients can be done on the key attributes of age, gender, location, and related parameters. The selection of LM-based implementation with ANN is done as it is the faster approach compared to other functions in neural networks.

scholarly journals Unsupervised functional neurocartography of the Aplysia buccal ganglion

2021 ◽  
Author(s):  
Renan M Costa ◽  
Vijay A Dharmaraj ◽  
Ryota Homma ◽  
Curtis L Neveu ◽  
William B Kristan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Motor Pattern ◽  
Pattern Generation ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Buccal Ganglion ◽  
Multiple Features ◽  
Selection Of

A major limitation of large-scale neuronal recordings is the difficulty in locating the same neuron in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semi-supervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. A matching method was developed based on a cyclic matching algorithm that allows for unsupervised extraction of groups of neurons, thereby enhancing scalability of the analysis. Cyclic matching was also used to automate the selection of high-quality matches, which allowed for unsupervised implementation of the machine learning algorithm. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, as well as helps to adapt this framework to other systems.

scholarly journals Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

2019 ◽  
Author(s):  
WooSeok Jeong ◽  
Samuel J. Stoneburner ◽  
Daniel King ◽  
Ruye Li ◽  
Andrew Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Bond ◽  
Density Functional ◽  
Large Scale ◽  
Black Box ◽  
Target System ◽  
Complete Active Space ◽  
Active Space ◽  
Bond Dissociation ◽  
Selection Of

<div>Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a “black-box” mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.</div>

Platelet Anti-Aggregating Activity and Tolerance of Clopidogrel in Atherosclerotic Patients

1996 ◽  
Vol 76 (06) ◽  
pp. 0939-0943 ◽  
Author(s):  
B Boneu ◽  
G Destelle ◽  
Keyword(s):  
Platelet Aggregation ◽  
Large Scale ◽  
Bleeding Time ◽  
Antithrombotic Activity ◽  
Ambulatory Patients ◽  
Patients At Risk ◽  
Set Up ◽  
Ischemic Events ◽  
Large Scale Clinical Trial ◽  
Selection Of

SummaryThe anti-aggregating activity of five rising doses of clopidogrel has been compared to that of ticlopidine in atherosclerotic patients. The aim of this study was to determine the dose of clopidogrel which should be tested in a large scale clinical trial of secondary prevention of ischemic events in patients suffering from vascular manifestations of atherosclerosis [CAPRIE (Clopidogrel vs Aspirin in Patients at Risk of Ischemic Events) trial]. A multicenter study involving 9 haematological laboratories and 29 clinical centers was set up. One hundred and fifty ambulatory patients were randomized into one of the seven following groups: clopidogrel at doses of 10, 25, 50,75 or 100 mg OD, ticlopidine 250 mg BID or placebo. ADP and collagen-induced platelet aggregation tests were performed before starting treatment and after 7 and 28 days. Bleeding time was performed on days 0 and 28. Patients were seen on days 0, 7 and 28 to check the clinical and biological tolerability of the treatment. Clopidogrel exerted a dose-related inhibition of ADP-induced platelet aggregation and bleeding time prolongation. In the presence of ADP (5 \lM) this inhibition ranged between 29% and 44% in comparison to pretreatment values. The bleeding times were prolonged by 1.5 to 1.7 times. These effects were non significantly different from those produced by ticlopidine. The clinical tolerability was good or fair in 97.5% of the patients. No haematological adverse events were recorded. These results allowed the selection of 75 mg once a day to evaluate and compare the antithrombotic activity of clopidogrel to that of aspirin in the CAPRIE trial.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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