Albert Bifet University of W aikato, New Zealand LTCI, T el ecom ParisT ech, France abifet@waikato.ac.nz Abstract --Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine learning techniques are not able to deal with data whose statistics is subject to gradual or sudden changes without any warning. Massive Online Analysis (MOA) is the collective name, as well as a software library, for new learners that are able to manage data streams. In this paper, we present a research study on streaming rebalancing. Indeed, data streams can be imbalanced as static data, but there is not a method to rebalance them incrementally, one element at a time. For this reason we propose a new streaming approach able to rebalance data streams online. Our new methodology is evaluated against some synthetically generated datasets using prequential evaluation in order to demonstrate that it outperforms the existing approaches.

--In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics. As the volume of web-scale data grows exponentially over time, sparse predictive analytics inevitably involves dynamic and sequential features. However, existing FMbased models assume no temporal orders in the data, and are unable to capture the sequential dependencies or patterns within the dynamic features, impeding the performance and adaptivity of these methods. Hence, in this paper, we propose a novel Sequence-A ware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. As static features (e.g., user gender) and dynamic features (e.g., user interacted items) express different semantics, we innovatively devise a multi-view self-attention scheme that separately models the effect of static features, dynamic features and the mutual interactions between static and dynamic features in three different views. In SeqFM, we further map the learned representations of feature interactions to the desired output with a shared residual network. T o showcase the versatility and generalizability of SeqFM, we test SeqFM in three popular application scenarios for FMbased models, namely ranking, classification and regression tasks. Extensive experimental results on six large-scale datasets demonstrate the superior effectiveness and efficiency of SeqFM. As an important supervised learning scheme, predictive analytics play a pivotal role in various applications, ranging from recommender systems [1], [2] to financial analysis [3] and online advertising [4], [5]. In practice, the goal of predictive analytics is to learn a mapping function from the observed variables (i.e., features) to the desired output. When dealing with categorical features in predictive analytics, a common approach is to convert such features into one-hot encodings [6]-[8] so that standard regressors like logistic regression [9] and support vector machines [10] can be directly applied. Due to the large number of possible category variables, the converted one-hot features are usually of high dimensionality but sparse [11], and simply using raw features rarely provides optimal results. The interactions among multiple raw features are usually termed as cross features [7] (a.k.a.

Should we believe headlines claiming nearly half of all jobs will be lost to robots and artificial intelligence? We think not, and in a newly released study, we explain why. Headlines trumpeting massive job losses have been in abundance for five or so years. Most come from a common source. It is a single study, conducted in 2013 by Oxford University's Carl Benedict Frey and Michael Osborne.

Then it was your phone. Now governments in Australia want you to use facial verification to access government services, take public transport and even for your private viewing. Last month the joint standing committee on intelligence and security told the government it needed to rethink its plans for a national facial verification database built off people's passport and driver's licence photos. It said there weren't strong enough safeguards for citizens' privacy and security built into the legislation. Despite the concerns, Australian governments and agencies have come up with some creative reasons to justify the use of facial recognition and sell it to the public.

Cloud Machine Learning Market report offers detailed analysis and a five-year forecast for the global Cloud Machine Learning industry. Cloud Machine Learning market report delivers the insights which will shape your strategic planning as you estimate geographic, product or service expansion within the Cloud Machine Learning industry.. The Cloud Machine Learning market accounted for $XX million in 2018, and is expected to reach $XX million by 2024, registering a CAGR of YY% from 2019 to 2024. The global Cloud Machine Learning market is segmented based on product, end user, and region. Region wise, it is analyzed across North America (U.S., Canada, and Mexico), Europe (Germany, UK, Italy, Spain, France, and rest of Europe), Asia-Pacific (Japan, China, Australia, India, South Korea, Taiwan, and, rest of Asia-Pacific) and EMEA (Brazil, South Africa, Saudi Arabia, UAE, rest of EMEA). Ask more details or request custom reports to our experts at https://www.proaxivereports.com/pre-order/53269 Moreover, other factors that contribute toward the growth of the Cloud Machine Learning market include favorable government initiatives related to the use of Cloud Machine Learning.

Advances in artificial intelligence have created bots and machines that can potentially pass as humans if they interact with people exclusively through a digital medium. Recently, a team of computer science researchers have studied how robots/machines and humans interact when the humans believe that the robots are also human. As reported by ScienceDaily, the results of the study found that people find robots/chatbots more persuasive when they believe the bots are human. Talal Rahwan, the associate professor of Computer Science at NYU Abu Dhabi, has recently led a study that examined how robots and humans interact with each other. The results of the experiment were published in Nature Machine Intelligence in a report called Transparency-Efficiency Tradeoff in Human-Machine Cooperation. During the course of the study, test subjects were instructed to play a cooperative game with a partner, and the partner may be either a human or a bot.

Advances in artificial intelligence have created bots and machines that can potentially pass as humans if they interact with people exclusively through a digital medium. Recently, a team of computer science researchers have studied how robots/machines and humans interact when the humans believe that the robots are also human. As reported by ScienceDaily, the results of the study found that people find robots/chatbots more persuasive when they believe the bots are human. Talal Rahwan, the associate professor of Computer Science at NYU Abu Dhabi, has recently led a study that examined how robots and humans interact with each other. The results of the experiment were published in Nature Machine Intelligence in a report called Transparency-Efficiency Tradeoff in Human-Machine Cooperation. During the course of the study, test subjects were instructed to play a cooperative game with a partner, and the partner may be either a human or a bot.

Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenomenon known as \textit{extrapolation error}. This is often due to past data available in the replay buffer that may be quite different from the data distribution under the current policy. We argue that most off-policy learning methods fundamentally suffer from a \textit{state distribution shift} due to the mismatch between the state visitation distribution of the data collected by the behavior and target policies. This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms. In this work, we first do a systematic analysis of state distribution mismatch in off-policy learning, and then develop a novel off-policy policy optimization method to constraint the state distribution shift. To do this, we first estimate the state distribution based on features of the state, using a density estimator and then develop a novel constrained off-policy gradient objective that minimizes the state distribution shift. Our experimental results on continuous control tasks show that minimizing this distribution mismatch can significantly improve performance in most popular practical off-policy policy gradient algorithms.

Problems that claim several agents to find no-conflicts paths from their start locations to their destinations are named as cooperative pathfinding problems. This problem can be efficiently solved by the Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than some traditional algorithms, such as Optimal Anytime(OA), in sparse environments. However, MA-RRT* cannot effectively find solutions in relatively dense environments, cause some random samples in the free space cannot be explored by the rapidly random tree, which hinders the application of MA-RRT* in a more complicated real-world. This paper proposes an improved version of MA-RRT *, called Multi-agent RRT* Potential Field (MA-RRT*PF), an anytime algorithm that can efficiently guide the rapidly random tree to the free space in relatively dense environments. It works by incorporating a potential field to the GREEDY function to enhance the ability to avoid the obstacles. The results show that MA-RRT*PF performs much better than MA-RRT* in relatively dense environments in terms of scalability while still maintaining the solution quality.

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accuracy. However, it is still unclear where the effectiveness stems from. In this paper, our main contribution is to take a step further in understanding LayerNorm. Many of previous studies believe that the success of LayerNorm comes from forward normalization. Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients. Furthermore, we find that the parameters of LayerNorm, including the bias and gain, increase the risk of over-fitting and do not work in most cases. Experiments show that a simple version of LayerNorm (LayerNorm-simple) without the bias and gain outperforms LayerNorm on four datasets. It obtains the state-of-the-art performance on En-Vi machine translation. To address the over-fitting problem, we propose a new normalization method, Adaptive Normalization (AdaNorm), by replacing the bias and gain with a new transformation function. Experiments show that AdaNorm demonstrates better results than LayerNorm on seven out of eight datasets.