MIAMI – Two people were killed and 11 others wounded Sunday when a video game tournament competitor went on a shooting rampage before turning the gun on himself in the northern Florida city of Jacksonville, local police said. Sheriff Mike Williams named the suspect of the shooting at a Madden 19 American football eSports tournament as 24-year-old David Katz from Baltimore, Maryland. "There were three deceased individuals at the scene, one of those being the suspect, who took his own life," Williams told reporters. He said local fire and rescue transported nine victims -- seven of whom had gunshot wounds -- to local hospitals, while another two people who were shot took their own transportation to hospital. Williams said Katz was a competitor in the eSports tournament and used "at least one handgun" to carry out the shooting.

With the tremendous generation of data, there are more and more situations where observations are described by more than one view. This is for example the case with multilingual documents that convey the same information in different languages or images that are naturally described according to different set of features (for example SIFT, HOG, CNN etc). In this paper, we study the related machine learning problem that consists in finding an efficient classification model from different information sources that describe the observations. This topic, called multiview learning Atrey et al. [2010], Sun [2013], has been expanding over the past decade, spurred by the seminal work of Blum and Mitchell on co-training Blum and Mitchell [1998] (with only two views). The aim is to learn a classifier which performs better than classifiers trained over each view separately (called view-specific classifier).

Sparse component analysis (SCA) is the following problem: Given an input matrix $M$ and an integer $r$, find a dictionary $D$ with $r$ columns and a sparse matrix $B$ with $r$ rows such that $M \approx DB$. A key issue in SCA is identifiability, that is, characterizing the conditions under which $D$ and $B$ are essentially unique (that is, they are unique up to permutation and scaling of the columns of $D$ and rows of $B$). Although SCA has been vastly investigated in the last two decades, only a few works have tackled this issue in the deterministic scenario, and no work provides reasonable bounds in the minimum number of data points (that is, columns of $M$) that leads to identifiability. In this work, we provide new results in the deterministic scenario when the data has a low-rank structure, that is, when $D$ has rank $r$, drastically improving with respect to previous results. In particular, we show that if each column of $B$ contains at least $s$ zeros then $\mathcal{O}(r^3/s^2)$ data points are sufficient to obtain an essentially unique decomposition, as long as these data points are well spread among the subspaces spanned by $r-1$ columns of $D$. This implies for example that for a fixed proportion of zeros (constant and independent of $r$, e.g., 10\% of zero entries in $B$), one only requires $O(r)$ data points to guarantee identifiability.

Finding meaningful representations in data is a core challenge in modern machine learning as the performance in many goal-directed frameworks such as reinforcement learning or supervised learning is directly and strongly influenced by the quality of the former. Usually, features are either provided (e.g. by expert knowledge) or acquired through learning. Most currently successful approaches for either deep supervised learning [1] or reinforcement learning [2] rely on a training signal, i.e., a classification label or reward signal, to provide sufficient indication which features of the input data should be extracted to increase performance. However, in most real-world scenarios labels have to be acquired by expert knowledge and reward signals are sparse. In unsupervised representation learning one tries to find and apply a principle by which to extract meaning from data without assuming the availability of any goal-driven metrics. Examples for such principles are based on reconstruction error (principal component analysis, autoencoder networks [3]), statistical dependence of extracted features 1 (independent component analysis [4]), indistinguishability of synthetically generated data from samples of the input distribution (generative adversarial nets [5]), fitting the probability distribution of input data (variational autoencoders [6]), (graph-)neighborhood preservation (locally-linear embedding [7], Laplacian eigenmaps [8]), and temporal coherence (slow feature analysis [9], regularized slowness optimization [10]).

While neural networks have shown impressive performance on large datasets, applying these models to tasks where little data is available remains a challenging problem. In this paper we propose to use feature transfer in a zero-shot experimental setting on the task of semantic parsing. We first introduce a new method for learning the shared space between multiple domains based on the prediction of the domain label for each example. Our experiments support the superiority of this method in a zero-shot experimental setting in terms of accuracy metrics compared to state-of-the-art techniques. In the second part of this paper we study the impact of individual domains and examples on semantic parsing performance. We use influence functions to this aim and investigate the sensitivity of domain-label classification loss on each example. Our findings reveal that cross-domain adversarial attacks identify useful examples for training even from the domains the least similar to the target domain. Augmenting our training data with these influential examples further boosts our accuracy at both the token and the sequence level.

This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of language models together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of language models (specifically trained on a large Twitter dataset) to predict and classify emotions. Our system reached 1st place with a macro $\text{F}_1$ score of 0.7145.

Model-based reinforcement learning (RL) methods can be broadly categorized as global model methods, which depend on learning models that provide sensible predictions in a wide range of states, or local model methods, which iteratively refit simple models that are used for policy improvement. While predicting future states that will result from the current actions is difficult, local model methods only attempt to understand system dynamics in the neighborhood of the current policy, making it possible to produce local improvements without ever learning to predict accurately far into the future. The main idea in this paper is that we can learn representations that make it easy to retrospectively infer simple dynamics given the data from the current policy, thus enabling local models to be used for policy learning in complex systems. To that end, we focus on learning representations with probabilistic graphical model (PGM) structure, which allows us to devise an efficient local model method that infers dynamics from real-world rollouts with the PGM as a global prior. We compare our method to other model-based and model-free RL methods on a suite of robotics tasks, including manipulation tasks on a real Sawyer robotic arm directly from camera images. Videos of our results are available at https://sites.google.com/view/solar-iclips

Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this paper, we propose a multi-output multi-quantile deep learning approach for jointly modeling several conditional quantiles together with the conditional expectation as a way to provide a more complete "picture" of the predictive density in spatio-temporal problems. Using two large-scale datasets from the transportation domain, we empirically demonstrate that, by approaching the quantile regression problem from a multi-task learning perspective, it is possible to solve the embarrassing quantile crossings problem, while simultaneously significantly outperforming state-of-the-art quantile regression methods. Moreover, we show that jointly modeling the mean and several conditional quantiles not only provides a rich description about the predictive density that can capture heteroscedastic properties at a neglectable computational overhead, but also leads to improved predictions of the conditional expectation due to the extra information and a regularization effect induced by the added quantiles.

Smart devices of everyday use (such as smartphones and wearables) are increasingly integrated with sensors that provide immense amounts of information about a person's daily life such as behavior and context. The automatic and unobtrusive sensing of behavioral context can help develop solutions for assisted living, fitness tracking, sleep monitoring, and several other fields. Towards addressing this issue, we raise the question: can a machine learn to recognize a diverse set of contexts and activities in a real-life through joint learning from raw multi-modal signals (e.g. accelerometer, gyroscope and audio etc.)? In this paper, we propose a multi-stream temporal convolutional network to address the problem of multi-label behavioral context recognition. A four-stream network architecture handles learning from each modality with a contextualization module which incorporates extracted representations to infer a user's context. Our empirical evaluation suggests that a deep convolutional network trained end-to-end achieves an optimal recognition rate. Furthermore, the presented architecture can be extended to include similar sensors for performance improvements and handles missing modalities through multi-task learning without any manual feature engineering on highly imbalanced and sparsely labeled dataset.

To estimate the conditional probability functions based on the direct problem setting, V-matrix based method was proposed. We construct V-matrix based constrained quadratic programming problems for which the inequality constraints are inconsistent. In particular, we would like to present that the constrained quadratic optimization problem for conditional probability estimation using V-matrix method may not have a consistent solution always. A limitation of V-matrix based (Direct) Method of Solving Conditional Probability Function V-Matrix method of estimation of conditional probability function was defined in [1]. Here we present a limitationof thesame. Since the kernel matrices are known to be positive semi-definite, the constrains matrices for the above Quadratic Programming (QP) problem may not have full rank and there is no guarantee that the constraints will be consistent, hence no solution could be found.