LONDON: Artificial intelligence is set to swell the GCC and Egypt's economies to the tune of $320 billion by 2030, according to a report. Globally, the economic uplift could be to the magnitude of $15.7 trillion, more than the current output of China and India combined, according to a report by professional services firm PwC. Within that increase, $6.6 trillion is likely to come from increased productivity, while $9.1 trillion is likely to come from benefits to consumers. Artificial intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives. AI is rapidly evolving, with current technology including autopilots, digital assistants and chatbots.

In this paper, we study the Empirical Risk Minimization problem in the non-interactive local model of differential privacy. We first show that if the ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity, to achieve error $\alpha$, on the exponential of the dimensionality $p$ with base $1/\alpha$ ({\em i.e.,} $\alpha^{-p}$), which answers a question in \cite{smith2017interaction}. Our approach is based on Bernstein polynomial approximation. Then, we propose player-efficient algorithms with $1$-bit communication complexity and $O(1)$ computation cost for each player. The error bound is asymptotically the same as the original one. Also with additional assumptions we show a server efficient algorithm with polynomial running time. At last, we propose (efficient) non-interactive locally differential private algorithms, based on different types of polynomial approximations, for learning the set of k-way marginal queries and the set of smooth queries.

Structured prediction requires searching over a combinatorial number of structures. To tackle it, we introduce SparseMAP, a new method for sparse structured inference, together with corresponding loss functions. SparseMAP inference is able to automatically select only a few global structures: it is situated between MAP inference, which picks a single structure, and marginal inference, which assigns probability mass to all structures, including implausible ones. Importantly, SparseMAP can be computed using only calls to a MAP oracle, hence it is applicable even to problems where marginal inference is intractable, such as linear assignment. Moreover, thanks to the solution sparsity, gradient backpropagation is efficient regardless of the structure. SparseMAP thus enables us to augment deep neural networks with generic and sparse structured hidden layers. Experiments in dependency parsing and natural language inference reveal competitive accuracy, improved interpretability, and the ability to capture natural language ambiguities, which is attractive for pipeline systems.

This indicates that even protected networks can be unexpectedly vulnerable. This is a crucial problem for this specific line of research because the primary concern of these studies are security threats. To tackle this crucial problem, we aim to develop defense methods with theoretical guarantees. Our goal is to ensure the lower bounds on the size of adversarial perturbations that networks can never be deceived for each input. We refer to these lower bounds as certified invariant radii, or simply, invariant radii. To make them available in broad applications, there are two fundamental requirements to their calculation methods: 1. the minimality of assumptions on network structures, 2. the computational tractability. However, many existing approaches require strong assumptions and massive computational costs. For example, we could not ensure perturbation invariance for some network structures such as wide residual networks [42], which have been commonly used in the evaluations of defense methods. This work tackled this problem and we provide a widely applicable, yet, highly scalable method to ensure large invariant radii. Our basic idea is to bound the size of adversarial perturbations that networks can never be deceived Even though the concept of using the Lipschitz constant has already appeared in Szegedy et al. [37], how much certifications they can provide has not been studied well. We show we can ensure significantly larger invariant radii compared to a recent computationally efficient counterpart [32]. However, the size of certified invariant radii can still be insufficient to be practically meaningful in some cases. We addressed this issue with a novel training procedure that further strengthen perturbation invariance.

We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet screening identifies triplets which can be safely removed from the optimization problem without losing the optimality. Compared with existing safe screening studies, triplet screening is particularly significant because of (1) the huge number of possible triplets, and (2) the semi-definite constraint in the optimization. We derive several variants of screening rules, and analyze their relationships. Numerical experiments on benchmark datasets demonstrate the effectiveness of safe triplet screening.

In real-world applications, it is conceivable that only positive and unlabeled (PU) data are available for training a classifier. For instance, in land-cover image classification, images of urban regions can be easily labeled, while images of non-urban regions are difficult to annotate due to high diversity of non-urban regions containing, e.g., forest, seas, grasses, and soil (Li et al., 2011). To cope with such situations, PU classification has been actively studied (Letouzey et al., 2000; Elkan and Noto, 2008; du Plessis et al., 2015), and the state-of-the-art method allows us to systematically train deep neural networks only from PU data (Kiryo et al., 2017). However, existing PU classification methods typically require an estimate of the class-prior probability, and their performance is sensitive to the quality of class-prior estimation (Kiryo et al., 2017). Although various class-prior estimation methods from PU data have been proposed so far (du Plessis and Sugiyama, 2014; Ramaswamy et al., 2016; Jain et al., 2016; du Plessis et al., 2017; Northcutt et al., 2017), accurate estimation of the class-prior is still highly challenging particularly for high-dimensional data.

Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance can be significantly degraded because test data may follow a different distribution from training data. Previous DRSL explicitly considers the worst-case distribution shift by minimizing the adver-sarially reweighted training loss. In this paper, we theoretically analyze the previous DRSL in a classification scenario. We reveal a rather surprising fact that the previous DRSL ends up giving classifiers optimal for the training distribution even though it is designed to be robust to change from the training distribution. Motivated by our analysis, we also propose novel DRSL that overcomes this limitation. We establish its convergence property and demonstrate its effectiveness through experiments.

It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. A weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually and it is sensitive to generalization performance, although the ITML-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric. In this paper, we present a new formulation of metric learning algorithm in which the distance threshold is optimized together. Since the optimization is still in the Bregman projection framework, the Dykstra algorithm can be applied for optimization. A nonlinear equation has to be solved to project the solution onto a half-space in each iteration. Na\"{i}ve method takes $O(LMn^{3})$ computational time to solve the nonlinear equation. In this study, an efficient technique that can solve the nonlinear equation in $O(Mn^{3})$ has been discovered. We have proved that the root exists and is unique. We empirically show that the accuracy of pattern recognition for the proposed metric learning algorithm is comparable to the existing metric learning methods, yet the distance threshold is automatically tuned for the proposed metric learning algorithm.

Abstract-- Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D detection networks. To identify the 3D box, an effective model fitting algorithm is developed based on generalised car models and score maps. A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks. The 3D detection results based on these two networks are similar, demonstrating the flexibility of the proposed pipeline. The results rank second among the 3D detection algorithms, indicating its competencies in 3D detection. I. INTRODUCTION Vision-based car detection has been well developed and widely implemented using deep learning technologies. The KITTI [1] benchmark site reports that the state of the art algorithms are able to achieve 90% average precision (AP). However, for autonomous vehicles, car detection in 2D images is not sufficient to provide enough information for the vehicle to perform planning and decision making due to the lack of depth data.

NEW DELHI: The ministry of electronics and IT has formed four committees to prepare a roadmap for the national artificial intelligence programme. The committees will be headed by IIT directors and experts from industry bodies such as Nasscom, union minister for electronics and IT Ravi Shankar Prasad said. The four committees will be for citizen centric services; data platforms; skilling, reskilling and R&D; and legal regulatory and cybersecurity. The ministry will promote high level research in these areas, the minister said, adding: "We must learn new dimensions of AI." MeitY will also assist the government machinery to implement technologies such as AI and 3D printing, he said. It is also looking at the blockchain technology and working on developing standards and regulations around it.