Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours. To overcome this, we introduce a comprehensive tool suite for effective multi-fidelity Bayesian optimization and the analysis of its runs. The suite, written in Python, provides a simple way to specify complex design spaces, a robust and efficient combination of Bayesian optimization and HyperBand, and a comprehensive analysis of the optimization process and its outcomes.

Abstract--Multi-view learning improves the learning performance by utilizing multi-view data: data collected from mul tiple sources, or feature sets extracted from the same data source . This approach is suitable for primate brain state decoding using cortical neural signals. This is because the compleme ntary components of simultaneously recorded neural signals, loc al field potentials (LFPs) and action potentials (spikes), can be tr eated as two views. In this paper, we extended broad learning syste m (BLS), a recently proposed wide neural network architectur e, from single-view learning to multi-view learning, and vali dated its performance in monkey oculomotor decision decoding fro m medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in nonhuman primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approac h to classify the oculomotor decision, than several classica l and state-of-the-art single-view and multi-view learning app roaches. UL TIview learning attempts to improve the learning performance by utilizing multi-view data, which can be collected from multiple data sources, or different featu re sets extracted from the same data source. For example, in an invasive brain-machine interface (BMI) using electrode s [1], effective BMI cursor control can be achieved using acti on potentials (spikes), which are high-pass filtered neural si gnals, or local field potentials (LFPs), which are low-pass filtered neural signals measured from the same electrodes. The spike s and LFPs can represent two views of the same task. There have been a few studies on applying multi-view learning to human brain state decoding. Kandemir et al. [2] combined multi-task learning and multi-view learning i n decoding a user's affective state, by treating different ty pes He and D. Wu are with the Key Laboratory of Im age Processing and Intelligent Control (Huazhong University o f Science and Technology), Ministry of Education.

A goal shared by artificial intelligence and information retrieval is to create an oracle, that is, a machine that can answer our questions, no matter how difficult they are. A more limited, but still instrumental, version of this oracle is a question-answering system, in which an open-ended question is given to the machine, and an answer is produced based on the knowledge it has access to. Such systems already exist and are increasingly capable of answering complicated questions. This progress can be partially attributed to the recent success of machine learning and to the efficient methods for storing and retrieving information, most notably through web search engines. One can imagine that this general-purpose question-answering system can be built as a billion-parameters neural network trained end-to-end with a large number of pairs of questions and answers. We argue, however, that although this approach has been very successful for tasks such as machine translation, storing the world's knowledge as parameters of a learning machine can be very hard. A more efficient way is to train an artificial agent on how to use an external retrieval system to collect relevant information. This agent can leverage the effort that has been put into designing and running efficient storage and retrieval systems by learning how to best utilize them to accomplish a task. ...

Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by Deep-Mind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.

Icosoku is a challenging and interesting puzzle that exhibits highly symmetrical and combinatorial nature. In this paper, we pose the questions derived from the puzzle, but with more difficulty and generality. In addition, we also present a constraint programming model for the proposed questions, which can provide the answers to our first two questions. The purpose of this paper is to share our preliminary result and problems to encourage researchers in both group theory and constraint communities to consider this topic further.

This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is to reduce the number of fails in the resolution process, to improve the inferences made during search by the constraint solver by strengthening constraint propagation, and to maintain the level of propagation while reducing the cost of propagating the constraints. Our experimental results show improvements in terms of the resolution speed compared to the original CSPs and a competitiveness to the recent tabulation approach. Besides, our approach can be realized in a preprocessing step, and therefore wouldn't collide with redundancy constraints or parallel computing if implemented.

This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHA T dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5% and outperforms the current state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits @1. 1 Introduction Building a conversation system with intelligence is challenging. Response selection, which aims to select a potential response from a set of candidates given the context of a conversation, is an important technique to build retrieval-based chatbots (Zhou et al., 2018). Many previous studies on single-turn (Wang et al., 2013) or multi-turn response selection (Lowe et al., 2015; Zhou et al., 2018; Gu et al., 2019) rank response candidates according to their semantic relevance with the given context. With the emergence and popular use of personal assistants such as Apple Siri, Google Now and Microsoft Cortana, the techniques of making personalized dialogues has attracted much research attention in recent years (Li et al., 2016; Zhang et al., 2018; Mazar e et al., 2018). Zhang et al. (2018) constructed a PERSONA-CHA T dataset for building personalized dialogue agents, where each persona was represented as multiple sentences of profile description. An example dialogue conditioned on given profiles from this dataset is given in Table 1 for illustration.

We live in exponential times, and merely having a digital strategy focused on continuous innovation is no longer enough to thrive in a constantly changing world. To transform an organisation and contribute to building a secure and rewarding networked society, collaboration among employees, customers, business units and even things is increasingly becoming key. Especially with the availability of new technologies such as artificial intelligence, organisations now, more than ever before, need to focus on bringing together the different stakeholders to co-create the future. Big data empowers customers and employees, the Internet of Things will create vast amounts of data and connects all devices, while artificial intelligence creates new human-machine interactions. In today's world, every organisation is a data organisation, and AI is required to make sense of it all.

The UK's privacy watchdog has opened an investigation into the use of facial recognition cameras in a busy part of central London. The information commissioner, Elizabeth Denham, announced she would look into the technology being used in Granary Square, close to King's Cross station. Two days ago the mayor of London, Sadiq Khan, wrote to the development's owner demanding to know whether the company believed its use of facial recognition software in its CCTV systems was legal. The Information Commissioner's Office (ICO) said it was "deeply concerned about the growing use of facial recognition technology in public spaces" and was seeking detailed information about how it is used. "Scanning people's faces as they lawfully go about their daily lives in order to identify them is a potential threat to privacy that should concern us all," Denham said.

The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. "There's a lot of collaboration around these projects--as there should be for the diversity of thought and background needed to deal with complex problems--working with at least seven other U of M schools," Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. "That's one of the powers that we enjoy." One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability--when blood flow drops and deprives the body of oxygen--which is one of the most common causes of death for critically ill or injured patients.