| Title | Adam (2016) |
| Durations | 02h 02 min |
| File Name | adam_weWc3.mov |
| File Size | 942 MB |
| Released | 4 years 6 months 0 day ago |
| Grade | DAT 1440p BDRip |
Description
The Adam demo is a real-time-rendered short film created with the Unity engine by our demo team. It runs at 1440p on a GeForce GTX980 and was shown on the booths at our Unite Europe conference.
Adam (2016)
Genre: Animation, Science Fiction
Cast: Ovanes Torosian, Stanimir Stamatov
Crew: Veselin Efremov, Veselin Efremov, Veselin Efremov, Torbjorn Laedre, Krasimir Nechevski
Release: 2016-06-20
Budget: $1,418,775
Revenue: $84,104,198
Mold Maker: Dr. Kennith Rowe
Communication Equipment Repairer: Georgette Schowalter
Hydrologist: Kim Towne
Civil Engineering Technician: Myrtle McGlynn
Statistical Assistant: Benny Farrell
Soldering Machine Setter: Mohammed Glover V
Bailiff: Maegan Smith
Park Naturalist: Prof. Sophie Durgan III
Gaming Service Worker: Miss Alycia Borer III
Assessor: Mikel Ortiz
Production Control Manager: Alan Legros
Human Resource Manager: Rory Price
Record Clerk: Madyson Auer
Cast: Ovanes Torosian, Stanimir Stamatov
Crew: Veselin Efremov, Veselin Efremov, Veselin Efremov, Torbjorn Laedre, Krasimir Nechevski
Release: 2016-06-20
Budget: $1,418,775
Revenue: $84,104,198
Mold Maker: Dr. Kennith Rowe
Communication Equipment Repairer: Georgette Schowalter
Hydrologist: Kim Towne
Civil Engineering Technician: Myrtle McGlynn
Statistical Assistant: Benny Farrell
Soldering Machine Setter: Mohammed Glover V
Bailiff: Maegan Smith
Park Naturalist: Prof. Sophie Durgan III
Gaming Service Worker: Miss Alycia Borer III
Assessor: Mikel Ortiz
Production Control Manager: Alan Legros
Human Resource Manager: Rory Price
Record Clerk: Madyson Auer
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We introduce Adam, an algorithm for first-order gradient-based optimizationof stochastic objective functions, based on adaptive estimates of lower-ordermoments. The method is straightforward to implement, is computationallyefficient, has little memory requirements, is invariant to diagonal rescalingof the gradients, and is well suited for problems that are large in terms ofdata and/or parameters. The method is also appropriate for non-stationaryobjectives and problems with very noisy and/or sparse gradients. Thehyper-parameters have intuitive interpretations and typically require littletuning. Some connections to related algorithms, on which Adam was inspired, arediscussed. We also analyze the theoretical convergence properties of thealgorithm and provide a regret bound on the convergence rate that is comparableto the best known results under the online convex optimization framework.Empirical results demonstrate that Adam works well in practice and comparesfavorably to other stochastic optimizati.
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We introduce Adam, an algorithm for first-order gradient-based optimizationof stochastic objective functions, based on adaptive estimates of lower-ordermoments. The method is straightforward to implement, is computationallyefficient, has little memory requirements, is invariant to diagonal rescalingof the gradients, and is well suited for problems that are large in terms ofdata and/or parameters. The method is also appropriate for non-stationaryobjectives and problems with very noisy and/or sparse gradients. Thehyper-parameters have intuitive interpretations and typically require littletuning. Some connections to related algorithms, on which Adam was inspired, arediscussed. We also analyze the theoretical convergence properties of thealgorithm and provide a regret bound on the convergence rate that is comparableto the best known results under the online convex optimization framework.Empirical results demonstrate that Adam works well in practice and comparesfavorably to other stochastic optimizati.
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Shop the official site of New York designer, Adam Lippes. Explore our collection of luxury knitwear, outerwear, dresses, men's and women's basics, and more..
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Adam is a figure in the Book of Genesis in the Hebrew Bible, and in the Quran and Christian belief. According to the creation myth of the Abrahamic religions, ....
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