Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although the weighted $L_1$ norm as the regularization term is usually employed in the sparse convex clustering, this increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To tackle these problems, this paper proposes a Bayesian sparse convex clustering via the idea of Bayesian lasso and global-local shrinkage priors. We introduce Gibbs sampling algorithms for our method using scale mixtures of normals. The effectiveness of the proposed methods is shown in simulation studies and a real data analysis.

Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robotics is a significant potential application domain for many of these algorithms, but generating robot experience in the real world is expensive, especially when each task requires a lengthy online training procedure. Off-policy algorithms can in principle learn arbitrary tasks from a diverse enough fixed dataset. In this work, we evaluate popular exploration methods by generating robotics datasets for the purpose of learning to solve tasks completely offline without any further interaction in the real world. We present results on three popular continuous control tasks in simulation, as well as continuous control of a high-dimensional real robot arm. Code documenting all algorithms, experiments, and hyper-parameters is available at https://github.com/qutrobotlearning/batchlearning.

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications. This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt. In this paper a mechanism for {\em automatically} learning or biasing the learner's hypothesis space is introduced. It works by first learning an appropriate {\em internal representation} for a learning environment and then using that representation to bias the learner's hypothesis space for the learning of future tasks drawn from the same environment. An internal representation must be learnt by sampling from {\em many similar tasks}, not just a single task as occurs in ordinary machine learning. It is proved that the number of examples $m$ {\em per task} required to ensure good generalisation from a representation learner obeys $m = O(a+b/n)$ where $n$ is the number of tasks being learnt and $a$ and $b$ are constants. If the tasks are learnt independently ({\em i.e.} without a common representation) then $m=O(a+b)$. It is argued that for learning environments such as speech and character recognition $b\gg a$ and hence representation learning in these environments can potentially yield a drastic reduction in the number of examples required per task. It is also proved that if $n = O(b)$ (with $m=O(a+b/n)$) then the representation learnt will be good for learning novel tasks from the same environment, and that the number of examples required to generalise well on a novel task will be reduced to $O(a)$ (as opposed to $O(a+b)$ if no representation is used). It is shown that gradient descent can be used to train neural network representations and experiment results are reported providing strong qualitative support for the theoretical results.

The statistical study, the report outlines the Global Artificial Intelligence in Education System Industry including production, cost/profit, supply-demand, and import-export. The total market is further bifurcated into a company, by country, and by various segmentation for the competitive landscape study.

Key Questions Answered in this Report: • What are the trends in the global military artificial intelligence and cybernetics across different regions? Global Military Artificial Intelligence Market Forecast, 2019-2024 The Global Military Artificial Intelligence Market report projects the market to grow at a significant CAGR of 18.66% on the basis of value during the forecast period from 2019 to 2024. North America dominated the global military artificial intelligence market with a share of 48.23% in 2019. North America, including the major countries such as the U.S., is the most prominent region for the military artificial intelligence market. In North America, the U.S. acquired a major market share in 2019 due to the major deployment of counter measures in defense sector in the country.

What did you choose to do as a writer, then? I was very naive when it comes to writing a series. I had no idea what was going to happen. I wanted it to be a lighthearted, realistic tale and I also wanted it to have a sense of drama, emotion, and suspense. I had no idea what I should do with the main characters in the first place, but I knew I had to make it a lighthearted, realistic story that would have the main characters struggling to find their happiness and love.

Technology has advanced enormously in the 30 years since the introduction of the first Children Act, which shaped the UK's system of child safeguarding. Today a computer-generated analysis – "machine learning" that produces predictive analytics – can help social workers assess the probability of a child coming on to the at-risk register. It can also help show how they might prevent that happening. But with technological advances come dilemmas unimaginable back in 1989. Is it right for social workers to use computers to help promote the welfare of children in need?

The underlying dynamical system carries temporal information from one time step to another and captures potential dependencies among the terms of a sequence. Like other deep neural networks, the weights of an RNN are learned by gradient descent. For the input at a time step to affect the output at a later time step, the gradients must back-propagate through each step. Since a sequence can be quite long, RNNs are prone to suffer from vanishing or exploding gradients as described in (Bengio, Frasconi, and Simard 1993) and (Pas-canu, Mikolov, and Bengio 2013). One consequence of this well-known problem is the difficulty of the network to model input-output dependency over a large number of time steps. There have been many different architectures that are designed to mitigate this problem. The most popular RNN architectures such as LSTMs (Hochreiter and Schmidhu-ber 1997) and GRUs (Cho et al. 2014), incorporate a gating mechanism to explicitly retain or discard information.

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs worldwide. Evidence on the efficacy of non-cancer generic drugs being tested for cancer exists in scientific publications, but trying to manually identify and extract such evidence is intractable. In this paper, we introduce a system to automate this evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language modeling techniques and plan to treat them as baseline approaches for future improvement of individual components.