In statistics and machine learning, classification with abstention, also known as classification with a reject option, is an extension of the standard setting of classification, in which the learner is allowed to refuse a prediction for a given query instance; research on this setting dates back to early work by Chow (1970) and Hellman (1970) and remains to be an important topic till today (Cortes et al., 2016). For the learner, the main reason to abstain is a lack of certainty about the corresponding outcome--refusing or at least deferring a decision might then be better than taking a high risk of a wrong decision. Nowadays, there are many machine learning problems in which complex, structured predictions are sought (instead of scalar values, like in classification and regression). For such problems, the idea of abstaining from a prediction can be generalized toward partial abstention: Instead of predicting the entire structure, the learner predicts only parts of it, namely those for which it is certain enough. This idea has already been realized, for example, for the problem of label ranking, where predictions are rankings (Cheng et al., 2012).

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.

Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the tasks with new structures, it is required to collect new training data and repeat the time-consuming meta training procedure. This makes them inefficient or even inapplicable in learning to solve heterogeneous few-shot learning tasks. We thus develop a novel and principled HierarchicalMeta Learning (HML) method. Different from existing methods that only focus on optimizing the adaptability of a meta model to similar tasks, HML also explicitly optimizes its generalizability across heterogeneous tasks. To this end, HML first factorizes a set of similar training tasks into heterogeneous ones and trains the meta model over them at two levels to maximize adaptation and generalization performance respectively. The resultant model can then directly generalize to new tasks. Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.

It's common knowledge that Barack Obama met the woman who eventually became his wife, Michelle Robinson, when he came to work at her law firm as a summer associate. George W. Bush met the future Mrs. Bush, who was Laura Welch back then, at a barbecue and took her mini-golfing the next day. And we all remember that Bill and Hillary Clinton were law school sweethearts. The historical record is full of these president-and-first-lady origin stories: Harry Truman was just 6 when he met the woman he would go on to marry, in church. So it's only natural to ask how the current crop of presidential candidates' how-they-met stories stack up.

Are you a junior researcher with the potential to become a world-class machine learning scientist? Apply to become a Vector Institute Postdoctoral Fellow and conduct cutting-edge fundamental research in machine learning and deep learning algorithms and their applications. Postdoctoral fellows at the Vector Institute are junior researchers with the potential to become world-class researchers. Like postdoctoral researchers in a University lab, postdoctoral fellows at the Vector Institute are tasked with and supported in carrying out state-of-the-art research, publishing at the highest international level, and contributing to the academic life and reputation of the Institute. In addition, postdoctoral fellows at the Vector Institute have access to the resources of a well-funded institute dedicated solely to machine learning and deep learning, and are encouraged to work with any of our over 25 world-class faculty in machine learning and deep learning, though they will typically work primarily with 1–2 faculty members.

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video.

Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge. Before solving the tasks, the learner enters an unsupervised playing stage where it creates its own tasks to solve, tries to solve them, and saves any solutions (programs) to the background knowledge. After the playing stage is finished, the learner enters the supervised building stage where it tries to solve the user-supplied tasks and can reuse solutions learnt whilst playing. The idea is that playing allows the learner to discover reusable general programs on its own which can then help solve the user-supplied tasks. We claim that playing can improve learning performance. We show that playing can reduce the textual complexity of target concepts which in turn reduces the sample complexity of a learner. We implement our idea in Playgol, a new inductive logic programming system. We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance. We think that the idea of playing (or, more verbosely, unsupervised bootstrapping for supervised program induction) is an important contribution to the problem of developing program induction approaches that self-discover BK.

A new learning system developed by MIT researchers improves robots' abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch -- and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi. In robotic planning, physical simulators are models that capture how different materials respond to force. Robots are "trained" using the models, to predict the outcomes of their interactions with objects, such as pushing a solid box or poking deformable clay. But traditional learning-based simulators mainly focus on rigid objects and are unable to handle fluids or softer objects.

The number of robots around the world is increasing rapidly. And it's said that automation will threatening more than 800m jobs worldwide by 2030. In the UK, it's claimed robots will replace 3.6m workers by this date, which means one in five British jobs would be performed by an intelligent machine. Jobs in higher education are no exception – with recent studies showing a rapid advancement in the use of these technologies in universities. The full potential of these disruptive technologies is yet to be discovered, but their impact on teaching and learning is expected to be huge.