The Experts below are selected from a list of 172683 Experts worldwide ranked by ideXlab platform
Tom Gedeon - One of the best experts on this subject based on the ideXlab platform.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
Abhinav Dhall - One of the best experts on this subject based on the ideXlab platform.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
Marty Wattenberg - One of the best experts on this subject based on the ideXlab platform.
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NIPS - Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
1995Co-Authors: Ari Juels, Marty WattenbergAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's 11-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
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stochastic hillclimbing as a Baseline Method for evaluating genetic algorithms
Neural Information Processing Systems, 1995Co-Authors: Ari Juels, Marty WattenbergAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's 11-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
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Stochastic Hillclimbing as a Baseline Method for
1994Co-Authors: Marty Wattenberg, Ari JuelsAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza''s 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
Ari Juels - One of the best experts on this subject based on the ideXlab platform.
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NIPS - Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
1995Co-Authors: Ari Juels, Marty WattenbergAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's 11-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
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stochastic hillclimbing as a Baseline Method for evaluating genetic algorithms
Neural Information Processing Systems, 1995Co-Authors: Ari Juels, Marty WattenbergAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address two problems to which GAs have been applied in the literature: Koza's 11-multiplexer problem and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these two problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
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Stochastic Hillclimbing as a Baseline Method for
1994Co-Authors: Marty Wattenberg, Ari JuelsAbstract:We investigate the effectiveness of stochastic hillclimbing as a Baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza''s 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing Methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA.
O. Ramana V. Murthy - One of the best experts on this subject based on the ideXlab platform.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.
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video and image based emotion recognition challenges in the wild emotiw 2015
International Conference on Multimodal Interfaces, 2015Co-Authors: Abhinav Dhall, Jyoti Joshi, Roland Goecke, O. Ramana V. Murthy, Tom GedeonAbstract:The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, Baseline Method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.