The classification of time series data is a challenge common to all data-driven fields. However, there is no agreement about which are the most efficient techniques to group unlabeled time-ordered data. This is because a successful classification of time series patterns depends on the goal and the domain of interest, i.e. it is application-dependent. In this article, we study free-to-play game data. In this domain, clustering similar time series information is increasingly important due to the large amount of data collected by current mobile and web applications. We evaluate which methods cluster accurately time series of mobile games, focusing on player behavior data. We identify and validate several aspects of the clustering: the similarity measures and the representation techniques to reduce the high dimensionality of time series. As a robustness test, we compare various temporal datasets of player activity from two free-to-play video-games. With these techniques we extract temporal patterns of player behavior relevant for the evaluation of game events and game-business diagnosis. Our experiments provide intuitive visualizations to validate the results of the clustering and to determine the optimal number of clusters. Additionally, we assess the common characteristics of the players belonging to the same group. This study allows us to improve the understanding of player dynamics and churn behavior.

Lifted Relational Neural Networks (LRNNs [15]) are weighted sets of first-order rules, which are used to construct feed-forward neural networks from relational structures. A central characteristic of LRNNs is that a different neural network is constructed for each learning example, but crucially, the weights of these different neural networks are shared. This allows LRNNs to use neural networks for learning in relational domains, despite the fact that training examples may vary considerably in size and structure. In previous work, LRNNs have been learned from handcrafted rules. In such cases, only the weights of the first-order rules have to be learned from training data, which can be accomplished using a variant of back-propagation. The use of handcrafted rules offers a natural way to incorporate domain knowledge in the learning process. In some applications, however, (sufficient) domain knowledge is lacking and both the rules and their weights have to be learned from data. To this end, in this paper we introduce a structure learning method for LRNNs. Our proposed structure learning method proceeds in an iterative fashion.

Background: A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noise in the extracted features and increasing the performance of prediction. Methods: In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Results and Conclusions: Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

Plain vanilla K-means clustering is prone to produce unbalanced clusters and suffers from outlier sensitivity. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset. We cast this problem as a mixed-integer linear program (MILP) that admits tractable semidefinite and linear programming relaxations. We propose deterministic rounding schemes that transform the relaxed solutions to feasible solutions for the MILP. We also prove that these solutions are optimal in the MILP if a cluster separation condition holds.

Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models. Consequently, a wealth of GP approximation schemes have been developed over the last 15 years to address these key limitations. Many of these schemes employ a small set of pseudo data points to summarise the actual data. In this paper, we develop a new pseudo-point approximation framework using Power Expectation Propagation (Power EP) that unifies a large number of these pseudo-point approximations. Unlike much of the previous venerable work in this area, the new framework is built on standard methods for approximate inference (variational free-energy, EP and Power EP methods) rather than employing approximations to the probabilistic generative model itself. In this way, all of approximation is performed at `inference time' rather than at `modelling time' resolving awkward philosophical and empirical questions that trouble previous approaches. Crucially, we demonstrate that the new framework includes new pseudo-point approximation methods that outperform current approaches on regression and classification tasks.

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Google's most adorable product launch today is definitely its puck-sized Home Mini. At $49, it'll square up against Amazon's Dot, but like the Dot, it will act as a gateway smart speaker for those not willing to throw down bigger sums of money. I took a look at the Home Mini at Google's satellite London event, and if other speakers left you cold, this unassuming AI speaker might win you over. Google's miniature version hones down last year's Home into a smaller design, but with all of the smarts. The only discernible drawback during my brief hands-on is that sound was (understandably) less bassy, and not quite as loud.

Even though many of the devices from today's Google's Pixel 2 event had leaked beforehand, there was still plenty left to surprise. Leading the way were the Pixel 2 and Pixel 2 XL, but we also got our first look at the Google Home Max and Mini, the 2-in-1 Pixelbook and the new wireless Pixel Buds. Some will be available in the UK soon, others will take their time to make their way across the Atlantic. Here's how much some of that new gear is going to cost you. As expected, the Pixel 2 and Pixel 2 XL are vastly improved from last year's models.

Google has unveiled its first AI headphones with a smart assistant built in. Called PixelBuds, the new headphones will use Google's Assistant software - and can do everything from play music wirelessly to translate languages. The firm revealed the $149 headphones alongside a raft of new hardware including its Pixel 2 phones, two new AI speakers and even an AI camera. Called PixelBuds, the new headphones will use Google's Assistant software - and can do everything from play music wirelessly to translate languages. All of the the audio controls are in a touchpad on the right earbud.

A revolution is taking place in the GPU software stack in the fields of analytics, machine learning and deep learning, driven by NVIDIA's hardware innovation, that provides 100x more processing cores and 20x greater memory bandwidth than CPUs. However, systems and platforms are unable to harness these disruptive performance gains because they remain isolated from each other. The GPU Open Analytics Initiative (GOAI) and its first project, the GPU Data Frame (GDF) was created to allow seamless passing of data between processes. At this meetup, we'll explain how we have implemented an end-to-end machine learning powered by GOAI. We will show how GDFs break down the silos to enable interactive data exploration, model training, and model scoring, that is lightning-fast by virtue of avoiding any serialization overhead.