Karen Livescu mainly investigates Artificial intelligence, Speech recognition, Pattern recognition, Natural language processing and Word. Her study explores the link between Artificial intelligence and topics such as Machine learning that cross with problems in Variety. Her Speech recognition research is multidisciplinary, relying on both Artificial neural network, Embedding, Dynamic Bayesian network and Training set.
The study incorporates disciplines such as Correlation clustering, Determining the number of clusters in a data set and Single-linkage clustering in addition to Pattern recognition. In her study, which falls under the umbrella issue of Natural language processing, Named-entity recognition, Context, Phrase, Semantic resource and Leverage is strongly linked to Bigram. Her work carried out in the field of Word brings together such families of science as Sentence, Similarity, Task and Parsing.
Karen Livescu spends much of her time researching Speech recognition, Artificial intelligence, Natural language processing, Word and Pattern recognition. Her Speech recognition study incorporates themes from Pronunciation, Feature, Dynamic Bayesian network, Discriminative model and Fingerspelling. Her research integrates issues of Context, Machine learning and Task in her study of Artificial intelligence.
Her Natural language processing study combines topics from a wide range of disciplines, such as Variation, Data set and Phone. Her research in Word intersects with topics in Dynamic time warping, Recurrent neural network, Margin, Embedding and Vocabulary. Her work deals with themes such as Singular value decomposition, Feature learning and Kernel, which intersect with Canonical correlation.
Artificial intelligence, Natural language processing, Task, Speech recognition and Word are her primary areas of study. Her research investigates the connection with Artificial intelligence and areas like Machine learning which intersect with concerns in Generative model. Many of her research projects under Natural language processing are closely connected to Matching with Matching, tying the diverse disciplines of science together.
Her biological study deals with issues like Language model, which deal with fields such as State and Notation. Her Speech recognition study integrates concerns from other disciplines, such as Sign language, American Sign Language, Fingerspelling and Vocabulary. Her work on Sequence labeling as part of general Word study is frequently connected to Set and Term, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Karen Livescu mostly deals with Artificial intelligence, Natural language processing, Speech recognition, Task and Encoder. Many of her studies on Artificial intelligence apply to Constructed language as well. Her studies deal with areas such as Task analysis, Data set and Coreference as well as Natural language processing.
Her Speech recognition research is multidisciplinary, incorporating elements of Vocabulary and Word embedding. The various areas that she examines in her Task study include Embedding and Function. Karen Livescu combines subjects such as Context, Segmentation, Speech processing, External image and Semantics with her study of Visualization.
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Deep Canonical Correlation Analysis
Galen Andrew;Raman Arora;Jeff Bilmes;Karen Livescu.
international conference on machine learning (2013)
Multi-view clustering via canonical correlation analysis
Kamalika Chaudhuri;Sham M. Kakade;Karen Livescu;Karthik Sridharan.
international conference on machine learning (2009)
On Deep Multi-View Representation Learning
Weiran Wang;Raman Arora;Karen Livescu;Jeff Bilmes.
international conference on machine learning (2015)
Towards Universal Paraphrastic Sentence Embeddings
John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu.
international conference on learning representations (2016)
Tailoring Continuous Word Representations for Dependency Parsing
Mohit Bansal;Kevin Gimpel;Karen Livescu.
meeting of the association for computational linguistics (2014)
From Paraphrase Database to Compositional Paraphrase Model and Back
John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu.
Transactions of the Association for Computational Linguistics (2015)
Speech production knowledge in automatic speech recognition.
Simon King;Joe Frankel;Karen Livescu;Erik McDermott.
Journal of the Acoustical Society of America (2007)
Stochastic optimization for PCA and PLS
Raman Arora;Andrew Cotter;Karen Livescu;Nathan Srebro.
allerton conference on communication, control, and computing (2012)
Deep Multilingual Correlation for Improved Word Embeddings
Ang Lu;Weiran Wang;Mohit Bansal;Kevin Gimpel.
north american chapter of the association for computational linguistics (2015)
Landmark-based speech recognition: report of the 2004 Johns Hopkins summer workshop
M. Hasegawa-Johnson;J. Baker;S. Borys;K. Chen.
international conference on acoustics, speech, and signal processing (2005)
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