Group Norm for Learning Structured SVMs with Unstructured Latent Variables

Latent variables models have been applied to a number of computer vision problems. However, the complexity of the latent space is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to over fitting and are slower to perform inference with. The goal of this paper is to regularize the complexity of the latent space and learn which hidden states are really relevant for prediction. Specifically, we propose using group-sparsity-inducing regularizers such as ℓ[subscript 1]-ℓ[subscript 2] to estimate the parameters of Structured SVMs with unstructured latent variables. Our experiments on digit recognition and object detection show that our approach is indeed able to control the complexity of latent space without any significant loss in accuracy of the learnt model.Quanta Computer (Firm)Google (Firm

Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition

Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, stimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10%.Comment: Conference paper published at 2016 29th SIBGRAPI, Conference on Graphics, Patterns and Images (SIBGRAPI). 8 pages, 7 figure

Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence

Block coordinate descent (BCD) methods are widely-used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure. Three main algorithmic choices influence the performance of BCD methods: the block partitioning strategy, the block selection rule, and the block update rule. In this paper we explore all three of these building blocks and propose variations for each that can lead to significantly faster BCD methods. We (i) propose new greedy block-selection strategies that guarantee more progress per iteration than the Gauss-Southwell rule; (ii) explore practical issues like how to implement the new rules when using "variable" blocks; (iii) explore the use of message-passing to compute matrix or Newton updates efficiently on huge blocks for problems with a sparse dependency between variables; and (iv) consider optimal active manifold identification, which leads to bounds on the "active set complexity" of BCD methods and leads to superlinear convergence for certain problems with sparse solutions (and in some cases finite termination at an optimal solution). We support all of our findings with numerical results for the classic machine learning problems of least squares, logistic regression, multi-class logistic regression, label propagation, and L1-regularization

Entity Linking to Wikipedia : Grounding entity mentions in natural language text using thematic context distance and collective search

This thesis proposes new methods for entity linking in natural language text that assigns entity mentions in unstructured natural language text to the semi-structured encyclopedia Wikipedia. Doing so, entity linking grounds a mention to an encyclopedic entry in Wikipedia and embeds it into this Linked-Open-Data hub. This enables a higher level view on single documents, provides hints for further reading and may be used to add details from other sources. Furthermore, enriching text documents with such links simultaneously resolves the ambiguity of entity names. This ambiguity is an unsolved challenge for many text mining applications: one entity may be designated by a multitude of names and every mention may denote a multitude of entities. Resolving the ambiguity of entity names is thus a crucial step for entity based retrieval, an open problem for most information retrieval and extraction tasks. For instance, search engines relying on heuristic string matches often retrieve irrelevant results as they can not satisfyingly resolve ambiguity. Moreover, there is a huge number of entity mentions that can not be linked to Wikipedia since albeit of its size, Wikipedia has a restricted coverage. Earlier and current work often ignored this and consequently all mentions of uncovered entities. Other approaches handle only entity mentions of specific types or are focussed on English as target language. Apart from such restrictions, no method achieves perfect linking performance. These are the tasks approached in this thesis. We introduce new methods for candidate entity retrieval and candidate entity consolidation, the key components to recall and precision, exploiting both the vast amount of structured and unstructured information stored in Wikipedia. First, we propose a new contextual similarity measure based on latent topic distributions inferred from unstructured natural language text. We show that this thematic distance between mention and candidate entity contexts yields a lower linking error rate than purely word based distances. Being language independent, this method enables high performance entity linking in previously neglected languages such as German and French. This approach is especially suitable, albeit not restricted to link person names, the class of mentions with highest ambiguity. We next propose a new candidate retrieval method to enable successful entity linking also for other entities that are not referenced canonically or exhibit the thematic coherence of persons. We introduce collective search that uses the structured information encoded in Wikipedia’s hyperlink graph to arrive at sets of strongly related candidate entities. This enables us to better handle synonymy, one of the hardest problems in entity linking and not thoroughly treated in previous work. We emphasize on general applicability and evaluate this method on a broad collection of benchmark corpora both in a supervised as well as in an unsupervised setting. We show that candidate enhancement through collective search increases linking performance on nearly all of these corpora and that our method is the most stable compared to other state-of-the-art approaches. Presenting the first unification of diverse performance measures, we also make a step forward to the comparability of entity linking methods. In conclusion, we provide state-of-the-art entity linking methods for nearly all of the current use cases. When it comes to fine-tuning, we note that entity linking has subjective aspects and adaptions may be necessary depending on the task at hand

Mobile app recommendations using deep learning and big data

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts