Friday, May 22, 2020
GameGAN Explained!
This video explains the new Neural Game Engine GameGAN from researchers at NVIDIA! This paper uses Deep Learning to store Pacman inside of a learned world model such that you can play the game by sending actions to the generative neural network. This video will describe the problem and how the proposed solution through careful architecture and loss function design! Thanks for Watching! Please Subscribe! Paper Links: NVIDIA GameGAN Blog Post: https://ift.tt/2ypJkxf NVIDIA Quick video presenting GameGAN: https://www.youtube.com/watch?v=BYt6r8z6pUY World Models: https://ift.tt/2IYv5zG GauGAN (SPADE layer) demo video: https://www.youtube.com/watch?v=p5U4NgVGAwg Four Novel Approaches to Manipulating Fabric: https://ift.tt/2Zp6eQt Intuitively Understanding Variational Autoencoders: https://ift.tt/2BJBr5O How much Knowledge Can You Pack into the Parameters of a Language Model? https://ift.tt/2WRaJBs MuZero: https://ift.tt/2qKQkRA Neural Turing Machines: https://ift.tt/2ai9KUr GAN Compression: https://ift.tt/3ggTGQX CycleGAN: https://ift.tt/2opD3rk Yann LeCun's 2020 ICLR Keynote (Importance of multi-modal predictions mentioned in video): https://ift.tt/3dzm8vf Regularizing Trajectory Optimization with Denoising Autoencoders: https://ift.tt/2TwseVH
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