Efficient explorative data analysis systems must take into account both what a user knows and wants to know. This paper proposes a principled framework for interactive visual exploration of relations in data, through views most informative given the user's current knowledge and objectives. The user can input pre-existing knowledge of relations in the data and also formulate specific exploration interests, then taken into account in the exploration. The idea is to steer the exploration process towards the interests of the user, instead of showing uninteresting or already known relations. The user's knowledge is modelled by a distribution over data sets parametrised by subsets of rows and columns of data, called tile constraints. We provide a computationally efficient implementation of this concept based on constrained randomisation. Furthermore, we describe a novel dimensionality reduction method for finding the views most informative to the user, which at the limit of no background knowledge and with generic objectives reduces to PCA. We show that the method is suitable for interactive use and robust to noise, outperforms standard projection pursuit visualisation methods, and gives understandable and useful results in analysis of real-world data. We have released an open-source implementation of the framework.

Smooth, powerful, and small, Elixir is an excellent language for learning functional programming, and with this hands-on introduction, you'll discover just how powerful Elixir can be. Authors Simon St. Laurent and J. David Eisenberg show you how Elixir combines the robust functional programming of Erlang with an approach that looks more like Ruby, and includes powerful macro features for metaprogramming. Updated to cover Elixir 1.4, the second edition of this practical book helps you write simple Elixir programs by teaching one skill at a time. Once you pick up pattern matching, process-oriented programming, and other concepts, you'll understand why Elixir makes it easier to build concurrent and resilient programs that scale up and down with ease.

Accurate vehicle localization is a crucial step towards building effective Vehicle-to-Vehicle networks and automotive applications. Yet standard grade GPS data, such as that provided by mobile phones, is often noisy and exhibits significant localization errors in many urban areas. Approaches for accurate localization from imagery often rely on structure-based techniques, and thus are limited in scale and are expensive to compute. In this paper, we present a scalable visual localization approach geared for real-time performance. We propose a hybrid coarse-to-fine approach that leverages visual and GPS location cues. Our solution uses a self-supervised approach to learn a compact road image representation. This representation enables efficient visual retrieval and provides coarse localization cues, which are fused with vehicle ego-motion to obtain high accuracy location estimates. As a benchmark to evaluate the performance of our visual localization approach, we introduce a new large-scale driving dataset based on video and GPS data obtained from a large-scale network of connected dash-cams. Our experiments confirm that our approach is highly effective in challenging urban environments, reducing localization error by an order of magnitude.

Data augmentation is an indispensable technique to improve generalization and also to deal with imbalanced datasets. Recently, AutoAugment (Cubuk et al., 2019) has been proposed to automatically search augmentation policies from a dataset and has significantly improved performances on many image recognition tasks. However, its search method requires thousands of GPU hours to train even in a reduced setting. In this paper, we propose Fast AutoAugment algorithm that learns augmentation policies using a more efficient search strategy based on density matching. In comparison to AutoAugment, the proposed algorithm speeds up the search time by orders of magnitude while maintaining the comparable performances on the image recognition tasks with various models and datasets including CIFAR-10, CIFAR-100, and ImageNet.

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.

Vue.ai's tags have given us the exact level of granularity we need on our ecommerce platform. Our merchandisers and buyers have a lot of manual work while labelling products. They're also fixing data inconsistencies we receive from our external vendors, an expensive and time consuming process. With an automated catalog management tool like VueTag, we efficiently tag products, provide better product discovery for our shoppers, and speeden our go-to-market strategy

The convolution layer has been the dominant feature extractor in computer vision for years. However, the spatial aggregation in convolution is basically a pattern matching process that applies fixed filters which are inefficient at modeling visual elements with varying spatial distributions. This paper presents a new image feature extractor, called the local relation layer, that adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. With this relational approach, it can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference. A network built with local relation layers, called the Local Relation Network (LR-Net), is found to provide greater modeling capacity than its counterpart built with regular convolution on large-scale recognition tasks such as ImageNet classification.

Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open question to date. In this paper, we propose a novel embedded vision system in autonomous micro-robots, to recognize motion patterns in dynamic robot scenes. Here, the basic motion patterns are categorized into movements of looming (proximity), recession, translation, and other irrelevant ones. The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction. Images are transformed to spikes via spatiotemporal computations towards a switch function and decision making mechanisms, in order to invoke proper robot behaviors amongst collision avoidance, tracking and wandering, in dynamic robot scenes. Our robot experiments demonstrated two main contributions: (1) This neural vision system is effective to recognize the basic motion patterns corresponding to timely and proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots demonstrated the effectiveness in recognizing more abundant motion features for collision detection, which is a great improvement compared with former studies.

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified Best-versus-Second-Best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.

Recent years we have witnessed the great popularity of graph representation learning with success in not only node-level tasks such as node classification (Kipf & Welling, 2016a) and link prediction (Zhang & Chen, 2018) but also graph-level tasks such as graph classification (Ying et al., 2018) and graph similarity/distance computation (Bai et al., 2019). There has been a rich body of work (Belkin & Niyogi, 2003; Qiu et al., 2018) on node-level embeddings that turn each node in a graph into a vector preserving node-node proximity (similarity/distance). It is thus natural to raise the question: Can we embed an entire graph into a vector in an unsupervised way, and how? However, most existing methods for graph-level, i.e. whole-graph, embeddings assume a supervised model (Zhang & Chen, 2019), with only a few exceptions such as G KERNELS(Yanardag & Vishwanathan, 2015), which typically count subgraphs for a given graph and can be slow, and GRAPH2VEC(Narayanan et al., 2017), which is transductive. A key challenge facing designing an unsupervised graph-level embedding model is the lack of graphlevel signals in the training stage. Unlike node-level embedding which has a long history in utilizing the link structure of a graph to embed nodes, there lacks such natural proximity (similarity/distance) information between graphs. Supervised methods, therefore, typically resort to graph labels as guidance and use aggregation based methods, e.g. However, this assumption is problematic, as simple aggregation of node embeddings can only preserve limited graph-level properties, which is, however, often insufficient in measuring graphgraph proximity ("inter-graph" information). Inspired by the recent progress on graph proximity modeling (Ktena et al., 2017; Bai et al., 2019), we propose a novel framework, UG RAPHEMBthat employs multi-scale aggregations of node-level embeddings, guided by the graph-graph proximity defined by well-accepted and domain-agnostic graph proximity metrics such as Graph Edit Distance (GED) (Bunke, 1983), Maximum Common Subgraph (MCS) (Bunke & Shearer, 1998), etc. RAPHEMBis to learn high-quality graph-level representations in a completely unsupervised and inductive fashion: During training, it learns a function that maps a graph into a universal embedding space best preserving graph-graph proximity, so that after training, any new graph can be mapped to this embedding space by applying the learned function. Inspired by the recent success of pre-training methods in the text domain, such as ELMO (Peters et al., 2018), B RAPHEMBfirst computes the graph-graph proximity scores, (c) yielding a "hyper-level graph" where each node is a graph in the dataset, and each edge has a proximity score associated with it, representing its weight/strength. RAPHEMBthen trains a function that maps each graph into an embedding which preserves the proximity score.