The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. To deal with partial and noisy observations in that endeavor, machine learning representations of the surrogate model can be used within a Bayesian data assimilation framework. However, these approaches require to consider long time series of observational data, meant to be assimilated all together. This paper investigates the possibility to learn both the dynamics and the state online, i.e. to update their estimates at any time, in particular when new observations are acquired. The estimation is based on the ensemble Kalman filter (EnKF) family of algorithms using a rather simple representation for the surrogate model and state augmentation. We consider the implication of learning dynamics online through (i) a global EnKF, (i) a local EnKF and (iii) an iterative EnKF and we discuss in each case issues and algorithmic solutions. We then demonstrate numerically the efficiency and assess the accuracy of these methods using one-dimensional, one-scale and two-scale chaotic Lorenz models.

In this paper, we attempt to solve a long-lasting open question in non-positive definite (non-PD) kernels: does a given non-PD kernel can be decomposed into the difference of two PD kernels (termed as positive decomposition)? We consider this question in a distribution view by introducing the \emph{signed measure}, which transforms positive decomposition to measure decomposition: a series of non-PD kernels can be associated with the linear combination of specific finite Borel measures. In this manner, our distribution-based framework provides a sufficient and necessary condition to answer this open question. And this answer is also computationally implementable in practice to scale non-PD kernels in large sample cases, which allows us to devise the first random features algorithm to obtain an unbiased estimator. Interestingly, our framework shows that the popular neural tangent kernel (NTK) of a two-layer ReLU network on the unit sphere is non-PD, which expands the usage of classical non-PD kernels. Experimental results on several benchmark datasets verify the effectiveness of our algorithm over the existing methods.

Based on the hurricane struking Puerto Rico in 2017, we developed a transportable disaster response system "DroneGo" featuring a drone fleet capable of delivering medical package and videoing roads. Assuming equal weight for both mission, we take the capability of carrying out the former missions as a constraint and a starting point from which reconnaissance routes are built. The feasibility of fitting packages into cargo bay 1 or 2 is tested by genetic algorithm. In scenario where drones carry packages to and unloaded back, from specification of drones and loading weight can we derive the maximum reachable distance of each drone loaded. A k-means clustering algorithm is used for partitioning destinations and deriving centroids as locations of bases.

In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, whether we can keep the accuracy while improving the robustness, is conceptually and practically more interesting, since robust accuracy should be lower than standard accuracy for any model. In this paper, we show this direction is also promising. Firstly, we find even over-parameterized deep networks may still have insufficient model capacity, because adversarial training has an overwhelming smoothing effect. Secondly, given limited model capacity, we argue adversarial data should have unequal importance: geometrically speaking, a natural data point closer to/farther from the class boundary is less/more robust, and the corresponding adversarial data point should be assigned with larger/smaller weight. Finally, to implement the idea, we propose geometry-aware instance-reweighted adversarial training, where the weights are based on how difficult it is to attack a natural data point. Experiments show that our proposal boosts the robustness of standard adversarial training; combining two directions, we improve both robustness and accuracy of standard adversarial training.

Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question Answering (VQA). However, existing VQA models represent relationships as a combination of object-level visual features which constrain a model to express interactions between objects in a single domain, while the model is trying to solve a multi-modal task. In this paper, we propose a general purpose semantic relationship parser which generates a semantic feature vector for each subject-predicate-object triplet in an image, and a Mutual and Self Attention (MSA) mechanism that learns to identify relationship triplets that are important to answer the given question. To motivate the significance of semantic relationships, we show an oracle setting with ground-truth relationship triplets, where our model achieves a ~25% accuracy gain over the closest state-of-the-art model on the challenging GQA dataset. Further, with our semantic parser, we show that our model outperforms other comparable approaches on VQA and GQA datasets.

Multivariate time series (MTS) data are becoming increasingly ubiquitous in diverse domains, e.g., IoT systems, health informatics, and 5G networks. To obtain an effective representation of MTS data, it is not only essential to consider unpredictable dynamics and highly variable lengths of these data but also important to address the irregularities in the sampling rates of MTS. Existing parametric approaches rely on manual hyperparameter tuning and may cost a huge amount of labor effort. Therefore, it is desirable to learn the representation automatically and efficiently. To this end, we propose an autonomous representation learning approach for multivariate time series (TimeAutoML) with irregular sampling rates and variable lengths. As opposed to previous works, we first present a representation learning pipeline in which the configuration and hyperparameter optimization are fully automatic and can be tailored for various tasks, e.g., anomaly detection, clustering, etc. Next, a negative sample generation approach and an auxiliary classification task are developed and integrated within TimeAutoML to enhance its representation capability. Extensive empirical studies on real-world datasets demonstrate that the proposed TimeAutoML outperforms competing approaches on various tasks by a large margin. In fact, it achieves the best anomaly detection performance among all comparison algorithms on 78 out of all 85 UCR datasets, acquiring up to 20% performance improvement in terms of AUC score.

Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions.

If you are learning machine learning for getting a high profile data science job then you can't miss out learning these 11 best machine learning algorithms. Here, we will first go through supervised learning algorithms and then discuss about the unsupervised learning ones. While there are many more algorithms that are present in the arsenal of machine learning, our focus will be on the most popular machine learning algorithms. These ML algorithms are quite essential for developing predictive modeling and for carrying out classification and prediction. These ML algorithms are the most useful for carrying out prediction and classification in both supervised as well as unsupervised scenarios.

Introduction to Machine Learning with Case Study in Python Understand concepts of least square regression and Hypothesis testing to our model like t-test, ANOVA, F-test, R square What you'll learn In this course, you will learn concepts of linear regression. You will learn the approaches towards regression with case study. First we start with understanding linear equation and the optimization function value sum of squared errors. With that we find the values of the coefficient and makes least square regression. Then we starts building our linear regression in python.

Introduction to Deep Learning with TensorFlow 2.0 Advanced implementation of regression model and essential tasks to be performed like feature selection in TensorFlow 2.x Bestseller What you'll learn In this course, you will learn advanced linear regression technique process and with this you can able to build any regression problem. With this intuition we will work on project: Customer Revenue Prediction. Problem Statement: A large child education toy company which sells educational tablets and gaming systems both online and in retail stores wanted to analyse the customer data. The goal of the problem is determine the following objective as shown below. Data Analysis & Preprocessing: Analyze customer data and draw the insights w.r.t revenue and based on the insights we will do data preprocessing.