scholarly journals Approximate Optimal Transport for Continuous Densities with Copulas

2019 ◽  
Author(s):  
Jinjin Chi ◽  
Jihong Ouyang ◽  
Ximing Li ◽  
Yang Wang ◽  
Meng Wang
Keyword(s):  
Machine Learning ◽  
Stochastic Optimization ◽  
Image Retrieval ◽  
Learning Community ◽  
Optimal Transport ◽  
State Of The Art ◽  
Probability Distributions ◽  
Synthetic Data ◽  
Computational Burden ◽  
Copula Parameter

Optimal Transport (OT) formulates a powerful framework by comparing probability distributions, and it has increasingly attracted great attention within the machine learning community. However, it suffers from severe computational burden, due to the intractable objective with respect to the distributions of interest. Especially, there still exist very few attempts for continuous OT, i.e., OT for comparing continuous densities. To this end, we develop a novel continuous OT method, namely Copula OT (Cop-OT). The basic idea is to transform the primal objective of continuous OT into a tractable form with respect to the copula parameter, which can be efficiently solved by stochastic optimization with less time and memory requirements. Empirical results on real applications of image retrieval and synthetic data demonstrate that our Cop-OT can gain more accurate approximations to continuous OT values than the state-of-the-art baselines.

scholarly journals Learning Aggregation Functions

2021 ◽  
Author(s):  
Giovanni Pellegrini ◽  
Alessandro Tibo ◽  
Paolo Frasconi ◽  
Andrea Passerini ◽  
Manfred Jaeger
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
State Of The Art ◽  
Real Data ◽  
Universal Function ◽  
Aggregation Function ◽  
Function Representation ◽  
Aggregation Functions ◽  
Complex Functions ◽  
Latent Dimension

Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce LAF (Learning Aggregation Function), a learnable aggregator for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum) as well as more complex functions (e.g. variance and skewness). We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation, and can be effectively combined with attention-based architectures.

scholarly journals Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6816
Author(s):  
Jannis N. Kahlen ◽  
Michael Andres ◽  
Albert Moser
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
Electrical Equipment ◽  
Diagnostic Systems ◽  
Additional Information ◽  
Model Based ◽  
Augmentation Techniques ◽  
Abnormal Condition

Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.

scholarly journals Nearest centroid classification on a trapped ion quantum computer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sonika Johri ◽  
Shantanu Debnath ◽  
Avinash Mocherla ◽  
Alexandros SINGK ◽  
Anupam Prakash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Computer ◽  
State Of The Art ◽  
Synthetic Data ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Machine Learning ◽  
Real World Applications ◽  
Promising Area ◽  
Centroid Classifier

AbstractQuantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

scholarly journals Unsupervised Selective Rank Fusion for Content-based Image Retrieval

2020 ◽  
Author(s):  
Lucas Pascotti Valem ◽  
Daniel Carlos Guimarães Pedronette
Keyword(s):  
Machine Learning ◽  
Image Retrieval ◽  
State Of The Art ◽  
Training Data ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Machine Learning Methods ◽  
Retrieval Systems ◽  
Promising Solution ◽  
Distinct Features

The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the scenarios.

scholarly journals Robust Learning from Noisy Side-information by Semidefinite Programming

2019 ◽  
Author(s):  
En-Liang Hu ◽  
Quanming Yao
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Learning Community ◽  
Critical Points ◽  
Efficient Algorithm ◽  
State Of The Art ◽  
Side Information ◽  
Low Rank ◽  
Robust Learning ◽  
Speed And Accuracy

Robustness recently becomes one of the major concerns among machine learning community, since learning algorithms are usually vulnerable to outliers or corruptions. Motivated by such a trend and needs, we pursue robustness in semi-definite programming (SDP) in this paper. Specifically, this is done by replacing the commonly used squared loss with the more robust L1-loss in the low-rank SDP. However, the resulting objective becomes neither convex nor smooth. As no existing algorithms can be applied, we design an efficient algorithm, based on majorization-minimization, to optimize the objective. The proposed algorithm not only has cheap iterations and low space complexity but also theoretically converges to some critical points. Finally, empirical study shows that the new objective armed with proposed algorithm outperforms state-of-the-art in terms of both speed and accuracy.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

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