This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks, popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.

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The San Francisco-based company debuted a digital brand studio this week that lets clients customize their own digital person by choosing from a set of realistic CGI avatars and uploading conversational trees with natural language processing systems from Google or IBM. Founded by Academy Award-winning visual effects engineer Mark Sagar and entrepreneur Greg Cross at the University of Auckland in New Zealand, Soul Machines is part of a small but growing scene of startups exploring how life-like human avatars can be put to use in business contexts, whether as virtual influencers, extensions of celebrity personalities or for interpersonal interaction practice. "The objective here is not to replace people, it's to really focus on things that are very, very difficult for organizations to deliver, like infinitely scalable customer interactions or infinitely scalable customer support at a completely different level of economics," Cross said. The company has raised $47.5 million to date from investors including Hong Kong-based Horizon Ventures and Salesforce's venture capital arm. It has already worked with a select set of clients on customer support avatars, including a digital customer service rep for Air New Zealand named Sophie, a car salesperson named Sarah for Mercedes-Benz and a virtual financial advisor named Jamie for Australia and New Zealand Banking Group.

Despite its notable success in adversarial learning approaches to multi-domain task-oriented dialog system, training the dialog policy via adversarial inverse reinforcement learning often fails to balance the performance of the policy generator and reward estimator. During optimization, the reward estimator often overwhelms the policy generator and produces excessively uninformative gradients. We proposes the Variational Reward estimator Bottleneck (VRB), which is an effective regularization method that aims to constrain unproductive information flows between inputs and the reward estimator. The VRB focuses on capturing discriminative features, by exploiting information bottleneck on mutual information. Empirical results on a multi-domain task-oriented dialog dataset demonstrate that the VRB significantly outperforms previous methods.

This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.

The latest MacBook Pro, just released, means the entire Apple laptop range has now been refreshed with newer processors and, most importantly, the new Magic Keyboard. Apple's complete range of laptops offer striking design, sumptuous trackpads, excellent performance and gorgeous screens. The MacBook Air was the last to gain a Retina Display in late 2018. But there was one key ingredient which wasn't working quite as well as it should have been for many users: the keyboard. A few years back, Apple switched its keyboard mechanism from scissor-switch to butterfly.

Distributed representation of words has improved the performance for many natural language tasks. In many methods, however, only one meaning is considered for one label of a word, and multiple meanings of polysemous words depending on the context are rarely handled. Although research works have dealt with polysemous words, they determine the meanings of such words according to a batch of large documents. Hence, there are two problems with applying these methods to sequential sentences, as in a conversation that contains ambiguous expressions. The first problem is that the methods cannot sequentially deal with the interdependence between context and word interpretation, in which context is decided by word interpretations and the word interpretations are decided by the context. Context estimation must thus be performed in parallel to pursue multiple interpretations. The second problem is that the previous methods use large-scale sets of sentences for offline learning of new interpretations, and the steps of learning and inference are clearly separated. Such methods using offline learning cannot obtain new interpretations during a conversation. Hence, to dynamically estimate the conversation context and interpretations of polysemous words in sequential sentences, we propose a method of Simultaneous Contextualization And INterpreting (SCAIN) based on the traditional Simultaneous Localization And Mapping (SLAM) algorithm. By using the SCAIN algorithm, we can sequentially optimize the interdependence between context and word interpretation while obtaining new interpretations online. For experimental evaluation, we created two datasets: one from Wikipedia's disambiguation pages and the other from real conversations. For both datasets, the results confirmed that SCAIN could effectively achieve sequential optimization of the interdependence and acquisition of new interpretations.

We study the spectrum reconstruction technique. As is known to all, eigenvalues play an important role in many research fields and are foundation to many practical techniques such like PCA(Principal Component Analysis). We believe that related algorithms should perform better with more accurate spectrum estimation. There was an approximation formula proposed, however, they didn't give any proof. In our research, we show why the formula works. And when both number of features and dimension of space go to infinity, we find the order of error for the approximation formula, which is related to a constant $c$-the ratio of dimension of space and number of features.

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale, and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the similarities between entities are conserved. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise, and aids in their interpretation. We explore three diverse comparisons: Gene Methylation vs Gene Expression, evolution of language sounds vs word use, and country-level economic metrics vs cultural beliefs. The nonparametric approach is robust to noise and differences in scaling, and makes only weak assumptions about how the data were generated. It operates by decomposing similarities into two components: the'structural' component analogous to a clustering, and an underlying'relationship' between those structures. This allows a'structural comparison' between two similarity matrices using their predictability from'structure'. The software, CLARITY, is available as an R package from github.com/danjlawson/CLARITY.

Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural networks in a given huge search space. However, all sub-networks are usually evaluated using the same criterion; that is, early stopping on a small proportion of the training dataset, which is an inaccurate and highly complex approach. In contrast to conventional methods, here we present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. Given an arbitrary search space, we first extract feature representations of all sub-networks according to changes in parameters or output features of each layer, and then calculate the similarity between two different sampled networks based on the representations. Then, a k-means clustering is conducted to aggregate similar architectures into the same cluster, separately executing sub-network evaluation in each cluster. The best architecture in each cluster is later merged to obtain the optimal neural architecture. Experimental results conducted on several benchmarks illustrate that DC-NAS can overcome the inaccurate evaluation problem, achieving a $75.1\%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.