scholarly journals Gradient Boosts the Approximate Vanishing Ideal

2020 ◽  
Vol 34 (04) ◽  
pp. 4428-4435
Author(s):  
Hiroshi Kera ◽  
Yoshihiko Hasegawa
Keyword(s):  
Machine Learning ◽  
Computer Algebra ◽  
Polynomial Time ◽  
General Model ◽  
Noisy Data ◽  
Vanishing Ideal ◽  
Algebraic Algorithms ◽  
Monomial Order ◽  
Basis Construction ◽  
Construction Algorithms

In the last decade, the approximate vanishing ideal and its basis construction algorithms have been extensively studied in computer algebra and machine learning as a general model to reconstruct the algebraic variety on which noisy data approximately lie. In particular, the basis construction algorithms developed in machine learning are widely used in applications across many fields because of their monomial-order-free property; however, they lose many of the theoretical properties of computer-algebraic algorithms. In this paper, we propose general methods that equip monomial-order-free algorithms with several advantageous theoretical properties. Specifically, we exploit the gradient to (i) sidestep the spurious vanishing problem in polynomial time to remove symbolically trivial redundant bases, (ii) achieve consistent output with respect to the translation and scaling of input, and (iii) remove nontrivially redundant bases. The proposed methods work in a fully numerical manner, whereas existing algorithms require the awkward monomial order or exponentially costly (and mostly symbolic) computation to realize properties (i) and (iii). To our knowledge, property (ii) has not been achieved by any existing basis construction algorithm of the approximate vanishing ideal.

scholarly journals Machine learning modeling and predictive control of nonlinear processes using noisy data

AIChE Journal ◽  
2021 ◽  
Author(s):  
Zhe Wu ◽  
David Rincon ◽  
Junwei Luo ◽  
Panagiotis D. Christofides
Keyword(s):  
Machine Learning ◽  
Predictive Control ◽  
Noisy Data ◽  
Nonlinear Processes

scholarly journals mlr3proba: An R Package for Machine Learning in Survival Analysis

2021 ◽  
Author(s):  
Raphael Sonabend ◽  
Franz J Király ◽  
Andreas Bender ◽  
Bernd Bischl ◽  
Michel Lang
Keyword(s):  
Machine Learning ◽  
Survival Analysis ◽  
General Model ◽  
R Package ◽  
Survival Modeling ◽  
Model Tuning

Abstract Motivation As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering, and more. mlr3proba provides a comprehensive machine learning interface for survival analysis and connects with mlr3’s general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modeling and evaluation. Availability mlr3proba is available under an LGPL-3 license on CRAN and at https://github.com/mlr-org/mlr3proba, with further documentation at https://mlr3book.mlr-org.com/survival.html.

scholarly journals Generating Interactive Worlds with Text

2020 ◽  
Vol 34 (02) ◽  
pp. 1693-1700 ◽  
Author(s):  
Angela Fan ◽  
Jack Urbanek ◽  
Pratik Ringshia ◽  
Emily Dinan ◽  
Emma Qian ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Common Sense ◽  
Learning Approach ◽  
Adventure Game ◽  
Machine Learning Approach ◽  
Game Environment ◽  
Game Elements ◽  
Construction Algorithms ◽  
Game Environments

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

scholarly journals Implementation of Shor’s Algorithm in QISKIT by Quantum Computing using IBMQ

2021 ◽  
Vol 10 (2) ◽  
pp. 1166-1170
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Polynomial Time ◽  
Classical Algorithm ◽  
Shor's Algorithm ◽  
Quantum Machine Learning

Quantum machine learning is the combination of quantum computing and classical machine learning. It helps in solving the problems of one field to another field. Shor’s algorithm is used for factoring the integers in polynomial time. Since the bestknown classical algorithm requires super polynomial time to factor the product of two primes, the widely used cryptosystem, RSA, relies on factoring being impossible for large enough integers. In this paper we will focus on the quantum part of Shor’s algorithm, which actually solves the problem of period finding. In polynomial time factoring problem can be turned into a period finding problem so an efficient period finding algorithm can be used to factor integers efficiently.

Influence of Domain and Model Properties on the Reliability Estimates' Performance

2009 ◽  
Vol 5 (4) ◽  
pp. 58-76
Author(s):  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Mean Squared Error ◽  
Noisy Data ◽  
Data Set ◽  
Squared Error ◽  
Reliability Estimates ◽  
Risk Sensitive ◽  
Average Accuracy ◽  
Applications Of Machine Learning

In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the authors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

scholarly journals Using Machine Learning Techniques to Classify the Interference of HPC applications in Virtual Machines with Uncertain Data

2020 ◽  
Author(s):  
Rafaela Brum ◽  
Flavia Bernardini ◽  
Maicon Alves ◽  
Lúcia Maria Drummond
Keyword(s):  
Machine Learning ◽  
Virtual Machines ◽  
Uncertain Data ◽  
Noisy Data ◽  
Main Memory ◽  
Machine Learning Techniques ◽  
Shared Resources ◽  
Practical Solution ◽  
Exact Number ◽  
Learning Techniques

This work aims to predict the level of interference caused by concurrent access to shared resources, such as cache and main memory, that can drastically affect the performance of HPC applications executed in clouds, by using some well-known machine learning techniques. As the user does not know the exact number of resource accesses in practice, we propose a human-readable categorization of these accesses. The used dataset contains information about synthetic and real HPC applications, and, to reflect the uncertainty of the user categorization, we inserted some noisy data in it. Our results showed that our approach could correctly predict the level of interference in most cases, indicating that it can be a practical solution.

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