We propose a novel model for temporal detection and localization which allows the training of deep neural networks using only counts of event occurrences as training labels. This powerful weakly-supervised framework alleviates the burden of the imprecise and time-consuming process of annotating event locations in temporal data. Unlike existing methods, in which localization is explicitly achieved by design, our model learns localization implicitly as a byproduct of learning to count instances. This unique feature is a direct consequence of the model's theoretical properties. We validate the effectiveness of our approach in a number of experiments (drum hit and piano onset detection in audio, digit detection in images) and demonstrate performance comparable to that of fully-supervised state-of-the-art methods, despite much weaker training requirements.

One of the major challenges in most BI projects is to figure out a way to get clean data. This is true for both BI and Predictive Analytics projects. To improve the effectiveness of the data cleaning process, the current trend is to migrate from the manual data cleaning to more intelligent machine learning-based processes. Before we dig into figuring out how to handle missing values, it's critical to figure out the nature of the missing values. There are three possible types, depending on if there exists a relationship between the missing data with the other data in the dataset.

The future impacts of artificial intelligence (AI) on society and the labour force have been studied and reported extensively. In a recent book, AI Superpowers, Kai-Fu Lee, former president of Google China, wrote that 40 to 50 per cent of current jobs will be technically and economically viable with AI and automation over the next 15 years. Artificial intelligence refers to computer systems that collect, interpret and learn from external data to achieve specific goals and tasks. Unlike natural intelligence displayed by humans and animals, it is an artificial form of intelligence demonstrated by machines. This has raised questions about the ethics of AI decision-making and impacts of AI in the workplace.

The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising from simulation of plasma fusion in a tokamak.

We propose an efficient linear-time graph-based divisive cluster analysis approach called Sampling Clustering. It constructs a lite informative dendrogram by recursively dividing a graph into subgraphs. In each recursive call, a graph is sampled first with a set of vertices being removed to disconnect latent clusters, then condensed by adding edges to the remaining vertices to avoid graph fragmentation caused by vertex removals. We also present some sampling and condensing methods and discuss the effectiveness in this paper. Our implementations run in linear time and achieve outstanding performance on various types of datasets. Experimental results show that they outperform state-of-the-art clustering algorithms with significantly less computing resource requirements.

The typical student in our CRT will follow a structured PhD training programming comprising research methods training, supervisor-initiated research-specific training, a CRT-organised training module in artificial intelligence methods, work placements, and international scientific research laboratories. In terms of specific CRT-organised training in artificial intelligence, we will focus on six thematic areas in which the co-applicant team and supervisor group have particular expertise. In our choice of areas, we are making no attempt at comprehensive coverage of the field of AI. Instead, we are selecting strands that (a) will relate to likely student PhD topics, and (b) afford scope for the students to acquire a broad range of relevant skills. A typical student in our CRT will follow a structured PhD training programme that comprises four main elements: (i) Host-based research methods training; (ii) Supervisor-initiated research-specific training; (iii) CRT-organized training in Artificial Intelligence methods; and (iv) Work placements.

Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they're hosted, whether it's a publisher's site, a digital library, or an author's personal web page. Kaggle: A data science site that contains a variety of externally contributed to interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even Seattle pet licenses. Although the data sets are user-contributed and thus have varying levels of cleanliness, the vast majority are clean. VisualData: Discover computer vision datasets by category, it allows searchable queries.

We present a corpus and approach to deduce the difficulty of questions asked in a reading comprehension test. A feature-driven model is designed that associates each question with a difficulty level. This would eliminate the laborious task of manually annotating questions in a computerized testing environment. Experiments performed on our corpus show that our model can classify questions with a micro F-score of 0.68.

Operating with ignorance is an important concern of the Machine Learning research, especially when the objective is to discover knowledge from the imperfect data. Data mining (driven by appropriate knowledge discovery tools) is about processing available (observed, known and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples, which are not yet observed, known or understood. These tools traditionally take samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach and we suggest considering the things the other way around. What if the task would be as follows: how to learn a model based on our ignorance, i.e. by processing the shape of 'voids' within the available data space? Can we improve traditional classification by modeling also the ignorance? In this paper, we provide some algorithms for the discovery and visualizing of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance discovery in machine learning.