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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15 May 2018TL;DR: OpenFace 2.0 is an extension of OpenFace toolkit and is capable of more accurate facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.
Abstract: Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace 2.0 - a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace 2.0 is an extension of OpenFace toolkit and is capable of more accurate facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The computer vision algorithms which represent the core of OpenFace 2.0 demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. Finally, unlike a lot of modern approaches or toolkits, OpenFace 2.0 source code for training models and running them is freely available for research purposes.
1,107 citations
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TL;DR: In this article, a software framework running on processors onboard the new Uranus mobile robot is proposed to maintain a probabilistic, geometric map of the robot's surroundings as it moves.
Abstract: A numeric representation of uncertain and incomplete sensor knowledge called certainty grids was used successfully in several recent mobile robot control programs developed at the Carnegie-Mellon University Mobile Robot Laboratory (MRL). Certainty grids have proven to be a powerful and efficient unifying solution for sensor fusion, motion planning, landmark identification, and many other central problems. MRL had good early success with ad hoc formulas for updating grid cells with new information. A new Bayesian statistical foundation for the operations promises further improvement. MRL proposes to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves. The certainty grid representation will allow this map to be incrementally updated in a uniform way based on information coming from various sources, including sonar, stereo vision, proximity, and contact sensors. The approach can correctly model the fuzziness of each reading and, at the same time, combine multiple measurements to produce sharper map features; it can also deal correctly with uncertainties in the robot's motion. The map will be used by planning programs to choose clear paths, identify locations (by correlating maps), identify well-known and insufficiently sensed terrain, and perhaps identify objects by shape. The certainty grid representation can be extended in the time dimension and used to detect and track moving objects. Even the simplest versions of the idea allow us to fairly straightforwardly program the robot for tasks that have hitherto been out of reach. MRL looks forward to a program that can explore a region and return to its starting place, using map "snapshots" from its outbound journey to find its way back, even in the presence of disturbances of its motion and occasional changes in the terrain.
1,105 citations
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06 Nov 2000TL;DR: It is demonstrated that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not and may out-perform algorithms not using a split.
Abstract: Recently there has been signi cant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classi ers.
1,103 citations
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09 Aug 2003TL;DR: This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning, and presents results on a robotic laser tag problem as well as three test domains from the literature.
Abstract: This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points and then tracking the value and its derivative for those points only. By using stochastic trajectories to choose belief points, and by maintaining only one value hyper-plane per point, PBVI successfully solves large problems: we present results on a robotic laser tag problem as well as three test domains from the literature.
1,101 citations
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TL;DR: In this article, the authors found that organizations vary considerably in the rates at which they learn and that the reasons for the variation observed in organizational learning curves include organizational forgetting, employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.
Abstract: Large increases in productivity are typically realized as organizations gain experience in production. These "learning curves" have been found in many organizations. Organizations vary considerably in the rates at which they learn. Some organizations show remarkable productivity gains, whereas others show little or no learning. Reasons for the variation observed in organizational learning curves include organizational "forgetting," employee turnover, transfer of knowledge from other products and other organizations, and economies of scale.
1,100 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 |