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Results for "Urvashi Khandelwal"
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What Does BERT Look At? An Analysis of BERT's AttentionJun 11 2019Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs ... More AttentionRNN: A Structured Spatial Attention MechanismMay 22 2019Visual attention mechanisms have proven to be integrally important constituent components of many modern deep neural architectures. They provide an efficient and effective way to utilize visual information selectively, which has shown to be especially ... More Faster K-Means Cluster EstimationJan 17 2017There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every ... More Linear Bandits with Feature FeedbackMar 09 2019Mar 12 2019This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus ... More Linear Bandits with Feature FeedbackMar 09 2019This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus ... More VUPIC: Virtual Machine Usage Based Placement in IaaS CloudDec 01 2012Efficient resource allocation is one of the critical performance challenges in an Infrastructure as a Service (IaaS) cloud. Virtual machine (VM) placement and migration decision making methods are integral parts of these resource allocation mechanisms. ... More Equivalent Dual Theories for 3D N=2 SupergravityJul 11 2019N=2 three dimensional Supergravity with internal $R-$symmetry generators can be understood as a two dimensional chiral Wess-Zumino-Witten model. In this paper, we present the reduced phase space description of the theory, which turns out to be flat limit ... More Evolving Clustered Random NetworksAug 04 2008We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models ... More On the Simulation of Polynomial NARMAX ModelsOct 16 2018In this paper, we show that the common approach for simulation non-linear stochastic models, commonly used in system identification, via setting the noise contributions to zero results in a biased response. We also demonstrate that to achieve unbiased ... More Discovery of Shifting Patterns in Sequence ClassificationDec 19 2017In this paper, we investigate the multi-variate sequence classification problem from a multi-instance learning perspective. Real-world sequential data commonly show discriminative patterns only at specific time periods. For instance, we can identify a ... More Smart Radio Spectrum Management for Cognitive RadioAug 06 2011Today's wireless networks are characterized by fixed spectrum assignment policy. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. ... More MESH: A Flexible Distributed Hypergraph Processing SystemApr 01 2019With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, ... More MESH: A Flexible Distributed Hypergraph Processing SystemApr 01 2019May 10 2019With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, ... More Max-Margin Feature SelectionJun 14 2016Many machine learning applications such as in vision, biology and social networking deal with data in high dimensions. Feature selection is typically employed to select a subset of features which im- proves generalization accuracy as well as reduces the ... More Block CUR: Decomposing Matrices using Groups of ColumnsMar 17 2017Jul 09 2018A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual ... More