Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Our research
Content type
+
Downloads (453)
+
Events (442)
 
Groups (152)
+
News (2722)
 
People (747)
 
Projects (1101)
+
Publications (12524)
+
Videos (5663)
Labs
Research areas
Algorithms and theory47205 (335)
Communication and collaboration47188 (214)
Computational linguistics47189 (234)
Computational sciences47190 (220)
Computer systems and networking47191 (757)
Computer vision208594 (906)
Data mining and data management208595 (105)
Economics and computation47192 (104)
Education47193 (82)
Gaming47194 (76)
Graphics and multimedia47195 (233)
Hardware and devices47196 (215)
Health and well-being47197 (91)
Human-computer interaction47198 (886)
Machine learning and intelligence47200 (877)
Mobile computing208596 (54)
Quantum computing208597 (32)
Search, information retrieval, and knowledge management47199 (674)
Security and privacy47202 (309)
Social media208598 (42)
Social sciences47203 (261)
Software development, programming principles, tools, and languages47204 (620)
Speech recognition, synthesis, and dialog systems208599 (129)
Technology for emerging markets208600 (32)
1–25 of 877
Sort
Show 25 | 50 | 100
1234567Next 
Yanjie Fu, Yong Ge, Yu Zheng, Yao, Yanchi Liu, Hui Xiong, and Nicholas Jing Yuan

Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users opinions about estates from online user reviews and offline moving behaviors (e.g., taxi traces, smart card transactions, check-ins). While a variety of features could be extracted from these data, these features are intercorrelated and...

Publication details
Date: 1 December 2015
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Publication details
Date: 1 December 2015
Type: Article
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: 29 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 28 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 27 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
J. Valentin, V. Vineet, M.-M. Cheng, D. Kim, J. Shotton, P. Kohli, M. Niessner, A. Criminisi, S. Izadi, and P. Torr
Publication details
Date: 1 August 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
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
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
Kalin Ovtcharov, Olatunji Ruwase, Joo-Young Kim, Jeremy Fowers, Karin Strauss, and Eric Chung
Publication details
Date: 1 August 2015
Type: Proceedings
Publisher: HOTCHIPS
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
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
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
Harikrishna Narasimhan, Purushottam Kar, and Prateek Jain

Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure. Such measures have spurred much interest and pose specific challenges to learning algorithms since their non-additive nature precludes a direct application of well-studied large scale optimization methods such as stochastic gradient descent.

In...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: Journal of Machine Learning Research
Yu Wang, David Wipf, Jeong-Min Yun, Wei Chen, and Ian Wassell

Many machine learning and signal processing tasks involve computing sparse representations using an overcomplete set of features or basis vectors, with compressive sensing-based applications a notable example. While traditionally such problems have been solved individually for different tasks, this strategy ignores strong correlations that may be present in real world data. Consequently there has been a push to exploit these statistical dependencies by jointly solving a series of sparse linear inverse...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: Uncertainty in Artificial Intelligence (UAI)
Mohammad Raza, Sumit Gulwani, and Natasa Milic-Frayling
Publication details
Date: 1 July 2015
Type: Inproceeding
Bo Xin and David Wipf

Many applications require recovering a matrix of minimal rank within an affine constraint set, with matrix completion a notable special case. Because the problem is NP-hard in general, it is common to replace the matrix rank with the nuclear norm, which acts as a convenient convex surrogate. While elegant theoretical conditions elucidate when this replacement is likely to be successful, they are highly restrictive and convex algorithms fail when the ambient rank is too high or when the constraint set is...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: International Conference on Machine Learning (ICML)
Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
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)
Elad Yom-Tov, Ingemar Johansson-Cox, Vasileios Lampos, and Andrew C. Hayward

Knowledge of the secondary attack rate (SAR) and serial interval (SI) of influenza is important for assessing the severity of seasonal epidemics of the virus. To date, such estimates have required extensive surveys of target populations. Here, we propose a method for estimating the intrafamily SAR and SI from postings on the Twitter social network. This estimate is derived from a large number of people reporting ILI symptoms in them and\or their immediate family members.

We analyze data from the...

Publication details
Date: 9 June 2015
Type: Article
Publisher: Wiley
Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Meg Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT 2015)
1–25 of 877
Sort
Show 25 | 50 | 100
1234567Next 
> Our research