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Publication details
Date: 1 November 2015
Type: Technical report
Publisher: USENIX – Advanced Computing Systems Association
Number: MSR-TR-2015-59
C. Morrison, K. Huckvale, A. Sakar, P. Kontschieder, J. Dorn, S. Steinheimer, C. P. Kamm, J. Burggraaff, M. D'Souza, F. Dahlke, L. Kappos, B. Uitdehaag, A. Criminisi, and A. Sellen
Publication details
Date: 1 October 2015
Type: Inproceeding
J. Burggraaff, J. Dorn, M. D'Souza, C. P. Kamm, P. Tewarie, P. Kontschieder, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, L. Kappos, and B. M. J. Uitdehaag
Publication details
Date: 1 October 2015
Type: Inproceeding
M. D'Souza, J. Burggraaff, P. Kontschieder, J. Dorn, C.P.Kamm, S. Seinheimer, P. Tewarie, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, B Uitdehaag, and L. Kappos
Publication details
Date: 1 October 2015
Type: Inproceeding
H. Lombaert, A. Criminisi, and N. Ayache

This paper presents a new method for classifying surface data via spectral representations of shapes. Our approach benefits classification problems that involve data living on surfaces, such as in cortical parcellation. For instance, current methods for labeling cortical points into surface parcels often involve a slow mesh deformation toward pre-labeled atlases, requiring as much as 4 hours with the established FreeSurfer. This may burden neuroscience studies involving region-specific measurements....

Publication details
Date: 1 October 2015
Type: Inproceeding
Publisher: Springer
Vasileios Lampos, Elad Yom-Tov, Richard Pebody, and Ingemar J. Cox

Assessing the effect of a health-oriented intervention by traditional epidemiological methods is commonly based only on population segments that use healthcare services. Here we introduce a complementary framework for evaluating the impact of a targeted intervention, such as a vaccination campaign against an infectious disease, through a statistical analysis of usergenerated content submitted on web platforms. Using supervised learning, we derive a nonlinear regression model for estimating the...

Publication details
Date: 7 September 2015
Type: Article
Publisher: Springer
Yu Zheng

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field...

Publication details
Date: 1 September 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Dilek Hakkani-Tur, Yun-Cheng Ju, Geoffrey Zweig, and Gokhan Tur

Spoken language understanding (SLU) in today’s conversational systems focuses on recognizing a set of domains, intents, and associated arguments, that are determined by application developers. User requests that are not covered by these are usually directed to search engines, and may remain unhandled. We propose a method that aims to find common user intents amongst these uncovered, out-of-domain utterances, with the goal of supporting future phases of dialog system design. Our approach relies on...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: Interspeech 2015 Conference
Publication details
Date: 1 September 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong

In natural language understanding (NLU), a user utterance can be labeled differently depending on the domain or application (e.g., weather vs. calendar). Standard domain adaptation techniques are not directly applicable to take advantage of the existing annotations because they assume that the label set is invariant. We propose a solution based on label embeddings induced from canonical correlation analysis (CCA) that reduces the problem to a standard domain adaptation task and allows use of a number of...

Publication details
Date: 29 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Young-Bum Kim, Karl Stratos, and Ruhi Sarikaya

In this paper, we apply the concept of pre-training to hidden-unit conditional random
fields (HUCRFs) to enable learning on unlabeled data. We present a simple yet effective pre-training technique that learns to associate words with their clusters, which are obtained in an unsupervised manner. The learned parameters are then used to initialize the supervised learning process. We also propose a word clustering technique based on canonical correlation analysis (CCA) that is sensitive to multiple word...

Publication details
Date: 28 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Young-Bum Kim, Karl Stratos, Xiaohu Liu, and Ruhi Sarikaya

In this paper, we introduce the task of selecting compact lexicon from large, noisy gazetteers.
This scenario arises often in practice, in particular spoken language understanding (SLU).
We propose a simple and effective solution based on matrix decomposition techniques:
canonical correlation analysis (CCA) and rank-revealing QR (RRQR) factorization. CCA is first used to derive low-dimensional gazetteer embeddings from domain-specific search logs. Then RRQR is used to find a subset of...

Publication details
Date: 27 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li

In this paper, we forecast the reading of an air quality monitoring station in the next 48 hours, using a data-driven method that considers the current meteorological data, weather forecasts, and the air quality data of the station and that of other stations within a few hundred kilometers to the station. Our predictive model is comprised of four major components: 1) a linear regression-based temporal predictor to model the local factor of air quality, 2) a neural network-based spatial predictor...

Publication details
Date: 1 August 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Chi Wang, Xueqing Liu, Yanglei Song, and Jiawei Han

Automatic construction of user-desired topical hierarchies over large volumes of text data is a highly desirable but challenging task. This study proposes to give users freedom to construct topical hierarchies via interactive operations such as expanding a branch and merging several branches. Existing hierarchical topic modeling techniques are inadequate for this purpose because (1) they cannot consistently preserve the topics when the hierarchy structure is modified; and (2) the slow inference prevents...

Publication details
Date: 1 August 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, and Eric Chung
Publication details
Date: 1 August 2015
Type: Proceedings
Publisher: HOTCHIPS
Publication details
Date: 1 August 2015
Type: Inproceeding
Publisher: KDD
Gerard Pons-Moll, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon

We present a new method for inferring dense data to model correspondences, focusing on the application of human pose estimation from depth images. Recent work proposed the use of regression forests to quickly predict correspondences between depth pixels and points on a 3D human mesh model. That work, however, used a proxy forest training objective based on the classification of depth pixels to body parts. In contrast, we introduce Metric Space Information Gain (MSIG), a new decision forest training...

Publication details
Date: 1 August 2015
Type: Article
Publisher: Springer
J. Valentin, V. Vineet, M.-M. Cheng, D. Kim, J. Shotton, P. Kohli, M. Niessner, A. Criminisi, S. Izadi, and P. Torr

We present a new interactive and online approach to 3D scene understanding. Our system, SemanticPaint, allows users to simultaneously scan their environment, whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the environment. Unlike offline systems, where capture, labeling and batch learning often takes hours or even days to perform, our approach is fully...

Publication details
Date: 1 August 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Bhaskar Mitra

Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "san francisco" → "san francisco 49ers" is semantically similar to "detroit" →"detroit lions". Likewise, "london"→"things to do in london" and "new york"→"new york tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" → "new movies" and "york" → "new york", however, are clearly different despite the lexical similarities in the two...

Publication details
Date: 1 August 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Timothy Baldwin, Marie Catherine de Marneffe, Bo Han, Young-Bum Kim, Alan Ritter, and Wei Xu

This paper presents the results of the two shared tasks associated with W-NUT 2015: (1) a text normalization task with 10 participants; and (2) a named entity tagging task with 8 participants. We outline the task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.

Publication details
Date: 1 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 28 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, and Masrour Zoghi

We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner’s goal is to find the best policy, or way of behaving, in some space of policies, although “best” is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game...

Publication details
Date: 1 July 2015
Type: Inproceeding
Yu Wang, David Wipf, Qing Ling, Wei Chen, and Ian Wassell

Subspace segmentation is the process of clustering a set of data points that are assumed to lie on the union of multiple linear or affine subspaces, and is increasingly being recognized as a fundamental tool for data analysis in high dimensional settings. Arguably one of the most successful approaches is based on the observation that the sparsest representation of a given point with respect to a dictionary formed by the others involves nonzero coefficients associated with points originating in the same...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: International Conference on Machine Learning (ICML)
Publication details
Date: 1 July 2015
Type: Technical report
Number: MSR-TR-2015-60
Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain

The problem of maximizing precision at the top of a ranked list, often dubbed Precision@k (prec@k), finds relevance in myriad learning applications such as ranking, multi-label classification, and learning with severe label imbalance. However, despite its popularity, there exist significant gaps in our understanding of this problem and its associated performance measure.

The most notable of these is the lack of a convex upper bounding surrogate for prec@k. We also lack scalable perceptron and...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: Journal of Machine Learning Research
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