Results 11 - 20
of
220
sorting: Bayesian clustering of nonstationary data
- In: Proceedings of the 18th International Conference on Neural Information Processing Systems
, 2004
"... Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Ga ..."
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Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partly due to the non-stationary nature of the data. We propose an automated technique for the clustering of non-stationary Gaussian sources in a Bayesian framework. At a first search stage, data is divided into short time frames and candidate descriptions of the data as a mixture of Gaussians are computed for each frame. At a second stage transition probabilities between candidate mixtures are computed, and a globally optimal clustering is found as the MAP solution of the resulting probabilistic model. Transition probabilities are computed using local stationarity assumptions and are based on a Gaussian version of the Jensen-Shannon divergence. The method was applied to several recordings. The performance appeared almost indistinguishable from humans in a wide range of scenarios, including movement, merges, and splits of clusters. 1
Fairness-aware radio resource management in downlink OFDMA cellular relay networks
- IEEE Trans. Wireless Commun
, 2010
"... Abstract — Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availa ..."
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Abstract — Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. Although distributed schemes are more attractive, it is essential to seek outstanding performance benchmarks to which various decentralized schemes can be compared. Therefore, this paper provides a comprehensive centralized RRM algorithm for downlink OFDMA cellular fixed relay networks in a way to ensure user fairness with minimal impact on network throughput. In contrast, it has been observed that pure opportunistic schemes and fairness-aware schemes relying solely on achievable and allocated capacities may not attain the desired fairness, e.g., proportional fair scheduling. The proposed scheme is queueaware and performs three functions jointly; dynamic routing, fair scheduling, and load balancing among cell nodes. We show that the proposed centralized scheme is different from the traditional centralized schemes in terms of the substantial savings in complexity and feedback overhead. Index Terms—RRM, OFDMA, relaying, routing, scheduling, fairness, load balancing, proportional fairness. I.
Deciphering foreign language by combining language models and context vectors
- In Proceedings of the Conference of the Association for Computational Linguistics (ACL
, 2012
"... In this paper we show how to train statistical machine translation systems on reallife tasks using only non-parallel monolingual data from two languages. We present a modification of the method shown in (Ravi and Knight, 2011) that is scalable to vocabulary sizes of several thousand words. On the ta ..."
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In this paper we show how to train statistical machine translation systems on reallife tasks using only non-parallel monolingual data from two languages. We present a modification of the method shown in (Ravi and Knight, 2011) that is scalable to vocabulary sizes of several thousand words. On the task shown in (Ravi and Knight, 2011) we obtain better results with only 5 % of the computational effort when running our method with an n-gram language model. The efficiency improvement of our method allows us to run experiments with vocabulary sizes of around 5,000 words, such as a non-parallel version of the VERBMOBIL corpus. We also report results using data from the monolingual French and English GIGAWORD corpora. 1
Analyzing the Errors of Unsupervised Learning
"... We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect biases of a model in a systematic way, (2) providing an efficient and robust method of measuring distance between two parame ..."
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We identify four types of errors that unsupervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incorrect biases of a model in a systematic way, (2) providing an efficient and robust method of measuring distance between two parameter settings of a model, and (3) showing that local optima issues which typically plague EM can be somewhat alleviated by increasing the number of training examples. We conduct our analyses on three models: the HMM, the PCFG, and a simple dependency model. 1
A kernel approach to comparing distributions
- IN: PROCEEDINGS OF THE TWENTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2007
"... We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether ..."
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We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.
Crowdsourcing step-by-step information extraction to enhance existing how-to videos
- In CHI ’14
, 2014
"... Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning expe ..."
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Cited by 10 (4 self)
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Millions of learners today use how-to videos to master new skills in a variety of domains. But browsing such videos is often tedious and inefficient because video player interfaces are not optimized for the unique step-by-step structure of such videos. This research aims to improve the learning experience of existing how-to videos with step-by-step annotations. We first performed a formative study to verify that annota tions are actually useful to learners. We created ToolScape, an interactive video player that displays step descriptions and intermediate result thumbnails in the video timeline. Learners in our study performed better and gained more self-efficacy using ToolScape versus a traditional video player. To add the needed step annotations to existing how-to videos at scale, we introduce a novel crowdsourcing workflow. It ex tracts step-by-step structure from an existing video, including step times, descriptions, and before and after images. We in troduce the Find-Verify-Expand design pattern for temporal and visual annotation, which applies clustering, text process ing, and visual analysis algorithms to merge crowd output. The workflow does not rely on domain-specific customiza tion, works on top of existing videos, and recruits untrained crowd workers. We evaluated the workflow with Mechanical Turk, using 75 cooking, makeup, and Photoshop videos on YouTube. Results show that our workflow can extract steps with a quality comparable to that of trained annotators across all three domains with 77 % precision and 81 % recall. Author Keywords Crowdsourcing; how-to videos; video annotation.
The Centrality of
, 1992
"... This Article is brought to you for free and open access by the Biochemistry, Department of at DigitalCommons@University of Nebraska- Lincoln. It ..."
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This Article is brought to you for free and open access by the Biochemistry, Department of at DigitalCommons@University of Nebraska- Lincoln. It
Assessing Optimal Assignment under Uncertainty: An Interval-based Algorithm
"... Abstract — We consider the problem of multi-robot taskallocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure ..."
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Cited by 9 (5 self)
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Abstract — We consider the problem of multi-robot taskallocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure of the uncertainty (e.g., a probability distribution, or moments of such distributions). We introduce a new O(n 4) algorithm, the Interval Hungarian algorithm, that extends the classic Kuhn-Munkres Hungarian algorithm to compute the maximum interval of deviation (for each entry in the assignment matrix) which will retain the same optimal assignment. This provides an efficient measurement of the tolerance of the allocation to the uncertainties, for both a specific interval and a set of interrelated intervals. We conduct experiments both in simulation and with physical robots to validate the approach and to gain insight into the effect of location uncertainty on allocations for multi-robot multi-target navigation tasks. I.
Lagrangian relaxation applied to sparse global network alignment
- In Pattern Recognition in Bioinformatics
, 2011
"... Abstract. Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of compara-tive network analysis, where one wants to identify commonalities between biological ..."
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Cited by 9 (0 self)
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Abstract. Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is lagging behind. This holds in particular for the field of compara-tive network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. In this paper we present a method for global network align-ment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. It is based on an integer linear programming formulation, generalizing the well-studied quadratic assignment problem. We obtain strong upper and lower bounds for the problem by improv-ing a Lagrangian relaxation approach and introduce the software tool natalie 2.0, a publicly available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative state-of-the-art methods with respect to quality and running time. 1
