Institution
Carnegie Mellon University
Education•Pittsburgh, Pennsylvania, United States•
About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors ask whether or not a supervised machine learning model will work in deployment, and what else can it tell you about the world, besides its predictive capabilities.
Abstract: Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?
1,197 citations
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20 Sep 2004TL;DR: In this paper, the Enron corpus is used as a new test bed for email folder prediction, and the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions.
Abstract: Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classifier, and using all the sections in combination with regression weights.
1,194 citations
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TL;DR: In this article, a mathematical model for communicating sequential processes is given, and a number of its interesting and useful properties are stated and proved, and the possibilities of nondetermimsm are fully taken into account.
Abstract: A mathematical model for communicating sequential processes is given, and a number of its interesting and useful properties are stated and proved. The possibilities of nondetermimsm are fully taken into account.
1,193 citations
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24 Aug 2008TL;DR: This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.
Abstract: Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users x #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations.
1,192 citations
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TL;DR: Atom transfer radical polymerization (ATRP) is one of the most successful methods to polymerize styrenes, (meth)acrylates and a variety of other monomers in a controlled fashion, yielding polymers with molecular weights predetermined by the ratio of the concentrations of consumed monomer to introduced initiator and with low polydispersities as discussed by the authors.
1,189 citations
Authors
Showing all 36645 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Robert C. Nichol | 187 | 851 | 162994 |
Michael I. Jordan | 176 | 1016 | 216204 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
J. N. Butler | 172 | 2525 | 175561 |
P. Chang | 170 | 2154 | 151783 |
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Yang Yang | 164 | 2704 | 144071 |
Geoffrey E. Hinton | 157 | 414 | 409047 |
Herbert A. Simon | 157 | 745 | 194597 |
Yongsun Kim | 156 | 2588 | 145619 |
Terrence J. Sejnowski | 155 | 845 | 117382 |
John B. Goodenough | 151 | 1064 | 113741 |
Scott Shenker | 150 | 454 | 118017 |