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Sample Of Training Proposal Letter

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(1909.08542.pdf)

3.43 MBEN 14 pages
Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature, with applications
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ссылка (2102.12092.pdf)

9.66 MBAditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, IlyaEN 20 pages
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset, involving complex architectures,
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Preview of Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)

5.64 MBEN 32 pages
Gradient flows are used to optimize functionals in probability spaces with the Wasserstein metric. A typical approach involves the Jordan-Kinderlehrer-Otto scheme, but this is challenging in high dimensions. We propose using input-convex neural networks to approximate the scheme.
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Preview of Online least-squares policy iteration for reinforcement learning control
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Online least-squares policy iteration for reinforcement learning control (10_009.pdf)

453 KBEN 7 pages
This technical report from Delft University of Technology's Center for Systems and Control presents an online least-squares policy iteration method for reinforcement learning control, published in the 2010 American Control Conference
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Preview of RoboCat: Foundation Agent for Robotic Manipulation
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RoboCat: Foundation Agent for Robotic Manipulation (robocat-a-self-improving-foundation-agent-for-robotic-manipulation.pdf)

24.84 MBEN 50 pages
A self-improving agent for robotic manipulation, developed by Google DeepMind, enables rapid skill acquisition across diverse robots and tasks.
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