Rice is the second most important cereal crop worldwide, and the first in terms of number of people who depend on it as a major staple food. Rice blast disease is the most important biotic constraint of rice cultivation causing each year millions of dollars of losses. Despite the efforts for breeding new resistant varieties, agricultural practices and chemical control are still the most important methods for disease management. Thus, rice blast forecasting is a primary tool to support rice growers in controlling the disease. In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared.

Gaussian processes provide a probabilistic framework for quantifying uncertainty of prediction and have been adopted in many applications in Statistics and Bayesian optimization. Unfortunately, they are hard to scale to large datasets as they necessitate inverting matrices whose size is linear in the number of observations. Moreover, they necessitate an a priori chosen functional form for their kernels with predetermined features. Our contribution is a framework that addresses both challenges. We use deep neural networks for automatic feature extraction, combined with explicit functional forms for the eigenspectrum of Gaussian processes with Gaussian kernels, to derive a Gaussian process inference and prediction framework whose complexity scales linearly in the number of observations and which accommodates automatic feature extraction. On a series of datasets, our method outperforms state of the art scalable Gaussian process approximations.

We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the tradeoff between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data and then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017. Our model outperforms the state of the art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.

We introduce tramp, standing for TRee Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides an unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such as generalized linear models, inference in multi-layer networks, matrix factorization, and reconstruction using non-separable penalties. For some models, the asymptotic performance of the algorithm can be theoretically predicted by the state evolution, and the measurements entropy estimated by the free entropy formalism. The implementation is modular by design: each module, which implements a factor, can be composed at will with other modules to solve complex inference tasks. The user only needs to declare the factor graph of the model: the inference algorithm, state evolution and entropy estimation are fully automated.

For a linear system, the response to a stimulus is often superposed by its responses to other decomposed stimuli. In quantum mechanics, a state is the superposition of multiple eigenstates. Here, by taking advantage of the phase difference, a common feature as we identified in data sets, we propose eigen component analysis (ECA), an interpretable linear learning model that incorporates the principle of quantum mechanics into the design of algorithm design for feature extraction, classification, dictionary and deep learning, and adversarial generation, etc. The simulation of ECA, possessing a measurable $class\text{-}label$ $\mathcal{H}$, on a classical computer outperforms the existing classical linear models. Eigen component analysis network (ECAN), a network of concatenated ECA models, enhances ECA and gains the potential to be not only integrated with nonlinear models, but also an interface for deep neural networks to implement on a quantum computer, by analogizing a data set as recordings of quantum states. Therefore, ECA and ECAN promise to expand the feasibility of linear learning models, by adopting the strategy of quantum machine learning to replace heavy nonlinear models with succinct linear operations in tackling complexity.

Purpose: In this study, the recently emerged advances in Fuzzy Cognitive Maps (FCM) are investigated and employed, for achieving the automatic and non-invasive diagnosis of Coronary Artery Disease (CAD). Methods: A Computer-Aided Diagnostic model for the acceptable and non-invasive prediction of CAD using the State Space Advanced FCM (AFCM) approach is proposed. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the system and the interpretability of the decision mechanism. The proposed method is tested utilizing a CAD dataset from the Laboratory of Nuclear Medicine of the University of Patras. More specifically, two architectures of AFCMs are designed, and different parameter testing is performed. Furthermore, the proposed AFCMs, which are based on the new equations proposed recently, are compared with the traditional FCM approach. Results: The experiments highlight the effectiveness of the AFCM approach and the new equations over the traditional approach, which obtained an accuracy of 78.21%, achieving an increase of seven percent (+7%) on the classification task, and obtaining 85.47% accuracy. Conclusions: It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease. Conclusions and future research related to recent pandemic of coronavirus are provided.

XGBoost is one of the most used libraries fora data science. At the time XGBoost came into existence, it was lightning fast compared to its nearest rival Python's Scikit-learn GBM. But as the times have progressed, it has been rivaled by some awesome libraries like LightGBM and Catboost, both on speed as well as accuracy. I, for one, use LightGBM for most of the use cases where I have just got CPU for training. But when I have a GPU or multiple GPUs at my disposal, I still love to train with XGBoost.

Enroll now in one of Udemy's machine learning courses ranging from beginner to advanced courses taught by industry experts. Are you intrigued by the idea of machine learning? Maybe you've applied core concepts in the workplace and want to take your artificial intelligence expertise to a higher level. An online machine learning course can equip you with the tools needed to understand the basics or accelerate your career. Take a quick look at Benzinga's top picks: Keep the following considerations in mind as you explore machine learning course options and choose the right one for you.

Machine Learning in Python: Building a Linear Regression Model In this video, I will be showing you how to build a linear regression model in Python using the scikit-learn package. We will be using the Diabetes dataset (built-in data from scikit-learn) and the Boston Housing (download from GitHub) dataset. This video is part of the [Python Data Science Project] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel. Disclaimer: Chanin is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to http://www.amazon.com.

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training. We present a set of methods for leveraging information about the semantic hierarchy embedded in class labels. We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions using order-preserving embeddings governed by both Euclidean and hyperbolic geometries, prevalent in natural language, and tailor them to hierarchical image classification and representation learning. We empirically validate all the models on the hierarchical ETHEC dataset.