In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs. CNN features have non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a convex cone and measure the geometric similarity of convex cones for classification. To establish this framework, we sequentially define multiple angles between two convex cones by repeating the alternating least squares method and then define the geometric similarity between the cones using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, maximizing the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, similar to a Fisher discriminant analysis. Finally, classification is based on the similarity between projected convex cones. The effectiveness of the proposed method was demonstrated experimentally using a private, multi-view hand shape dataset and two public databases.

Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve desired placement percentage. Business school data set is biased towards one class, i.e., imbalanced in nature. And learning from imbalanced data set is a difficult proposition. Most existing classification methods tend not to perform well on minority class examples when the data set is extremely imbalanced, because they aim to optimize the overall accuracy without considering the relative distribution of each class. The aim of the paper is twofold. We first propose an integrated sampling technique with an ensemble of classification tree (CT) and artificial neural network (ANN) model as one of the methodologies which works better compared to other similar methods. Further we propose a superensemble imbalanced classifier which works better on the original business school data set. Our proposed superensemble classifier not only handles the imbalance data set but also achieves higher accuracy in case of feature selection cum classification problems. The proposal has been compared with other state-of-the-art classifiers and found to be very competitive.

Most artificial intelligence models have limiting ability to solve new tasks faster, without forgetting previously acquired knowledge. The recently emerging paradigm of continual learning aims to solve this issue, in which the model learns various tasks in a sequential fashion. In this work, a novel approach for continual learning is proposed, which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies. We name it as Reinforced Continual Learning. Our method not only has good performance on preventing catastrophic forgetting but also fits new tasks well. The experiments on sequential classification tasks for variants of MNIST and CIFAR-100 datasets demonstrate that the proposed approach outperforms existing continual learning alternatives for deep networks.

The success of deep learning has been due in no small part to the availability of large annotated datasets. Thus, a major bottleneck in the current learning pipeline is the human annotation of data, which can be quite time consuming. For a given problem setting, we aim to circumvent this issue via the use of an externally specified energy function appropriate for that setting; we call this the "Deep Energy" approach. We show how to train a network on an entirely unlabelled dataset using such an energy function, and apply this general technique to learn CNNs for two specific tasks: seeded segmentation and image matting. Once the network parameters have been learned, we obtain a high-quality solution in a fast feed-forward style, without the need to repeatedly optimize the energy function for each image.

The representation power of similarity functions used in neural network-based graph embedding is considered. The inner product similarity (IPS) with feature vectors computed via neural networks is commonly used for representing the strength of association between two nodes. However, only a little work has been done on the representation capability of IPS. A very recent work shed light on the nature of IPS and reveals that IPS has the capability of approximating any positive definite (PD) similarities. However, a simple example demonstrates the fundamental limitation of IPS to approximate non-PD similarities. We then propose a novel model named Shifted IPS (SIPS) that approximates any Conditionally PD (CPD) similarities arbitrary well. CPD is a generalization of PD with many examples such as negative Poincare distance and negative Wasserstein distance, thus SIPS has a potential impact to significantly improve the applicability of graph embedding without taking great care in configuring the similarity function. Our numerical experiments demonstrate the SIPS's superiority over IPS. In theory, we further extend SIPS beyond CPD by considering the inner product in Minkowski space so that it approximates more general similarities.

Active Learning (AL) requires learners to retrain the classifier with the minimum human supervisions or labeling in the unlabeled data pool when the current training set is not enough. However, general AL sampling strategies with a few label support inevitably suffer from performance decrease. To identify which samples determine the performance of the classification hyperplane, Core Vector Machine (CVM) and Ball Vector Machine (BVM) use the geometry boundary points of each Minimum Enclosing Ball (MEB) to train the classification hypothesis. Their theoretical analysis and experimental results show that the improved classifiers not only converge faster but also obtain higher accuracies compared with Support Vector Machine (SVM). Inspired by this, we formulate the cluster boundary point detection issue as the MEB boundary problem after presenting a convincing proof of this observation. Because the enclosing ball boundary may have a high fitting ratio when it can not enclose the class tightly, we split the global ball problem into two kinds of small Local Minimum Enclosing Ball (LMEB): Boundary ball (B-ball) and Core ball (C-ball) to tackle its over-fitting problem. Through calculating the update of radius and center when extending the local ball space, we adopt the minimum update ball to obtain the geometric update optimization scheme of B-ball and C-ball. After proving their update relationship, we design the LEB (Local Enclosing Ball) algorithm using centers of B-ball of each class to detect the enclosing ball boundary points for AL sampling. Experimental and theoretical studies have shown that the classification accuracy, time, and space performance of our proposed method significantly are superior than the state-of-the-art algorithms.

Robustness to adversarial perturbation has become an extremely important criterion for applications of machine learning in security-sensitive domains such as spam detection [25], fraud detection [6], criminal justice [3], malware detection [13], and financial markets [27]. Systematic methods for generating adversarial examples by small perturbations of original input data, also known as "attack," have been developed to operationalize this criterion and to drive the development of more robust learning systems [4, 26, 7]. Most of the work in this area has focused on differentiable models with continuous input spaces [26, 7, 14, 14]. In this setting, the proposed attack strategies add a gradient-based perturbation to the original input. It has been shown that such perturbations can result in a dramatic decrease in the predictive accuracy of the model. Thus this line of research has demonstrated the vulnerability of deep neural networks to adversarial examples in tasks like image classification and speech recognition. We focus instead on adversarial attacks on models with discrete input data, such as text data, where each feature of an input sample has a categorical domain. While gradient-based approaches are not directly applicable to this setting, variations of gradient-based approaches have been shown effective in differentiable models. For example, Li et al. [15] proposed to locate the top features with the largest gradient magnitude of their embedding, and Papernot et al. [20] proposed to modify randomly selected features of an input through perturbing each feature by signs of the gradient, and project them onto the closest vector in the embedding space.

For better or worse, Google Assistant can do it all. From mundane tasks like turning on your lights and setting reminders to convincingly mimicking human speech patterns, the AI helper is so capable it's scary. Its latest (unofficial) ability, though, is a bit more sinister. Artist Alexander Reben recently taught Assistant to fire a gun. Fortunately, the victim was an apple, not a living being.

You don't have to wait for food delivery drones... if you live in the right part of China. Alibaba's online meal giant Ele.me has been cleared to use drones for delivering orders in Shanghai's Jinshan Industrial Park. The initiative won't deliver directly to your abode, but it will save you a lot of travel time: there are 17 routes, each of with two fixed drop-off points. Your food should arrive within 20 minutes, which isn't always possible with conventional cars slogging through traffic. Despite the automation, Ele.me believes this could be better for drivers.

As of 1/15, based on historical growth rates, it is estimated that 90% of USA Uber driver-partners drive for UberX.