In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L0 norm, however its optimization is NP-hard. Mixed norms, such as L1/L2 measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L1 norm. However, present algorithms designed for optimizing the mixed norm L1/L2 are slow and other formulations for sparse NMF have been proposed such as those based on L1 and L0 norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets.
Example Output: Block Coordinate Descent for Sparse NMF

Example Input: Deep networks rely on massive amounts of labeled data to learn powerful models. For a target task short of labeled data, transfer learning enables model adaptation from a different source domain. This paper addresses deep transfer learning under a more general scenario that the joint distributions of features and labels may change substantially across domains. Based on the theory of Hilbert space embedding of distributions, a novel joint distribution discrepancy is proposed to directly compare joint distributions across domains, eliminating the need of marginal-conditional factorization. Transfer learning is enabled in deep convolutional networks, where the dataset shifts may linger in multiple task-specific feature layers and the classifier layer. A set of joint adaptation networks are crafted to match the joint distributions of these layers across domains by minimizing the joint distribution discrepancy, which can be trained efficiently using back-propagation. Experiments show that the new approach yields state of the art results on standard domain adaptation datasets.
Example Output: Deep Transfer Learning with Joint Adaptation Networks

Example Input: We give solutions to two fundamental computational problems in ontologybased data access with the W3C standard ontology language OWL2QL: the succinctness problem for first-order rewritings of ontology-mediated queries (OMQs), and the complexity problem for OMQ answering. We classify OMQs according to the shape of their conjunctive queries (treewidth, the number of leaves) and the existential depth of their ontologies. For each of these classes, we determine the combined complexity of OMQ answering, and whether all OMQs in the class have polynomial-size first-order, positive existential, and nonrecursive datalog rewritings. We obtain the succinctness results using hypergraph programs, a new computational model for Boolean functions, which makes it possible to connect the size of OMQ rewritings and circuit complexity.
Example Output:
Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity