We convert all images to grayscale and resize to 84x84. It is a convolutional neural network with fixed random weights. In Atari, we use 128 parallel environments, and in Habitat, we use 1 environment, as it does not support multithreading. We use the same hyperparameters as in large scale curiosity: a learning rate of 0.0001 for all models, a discount factor Future prediction and multimodal association can be complementary forms of curiosity. Further work could explore other ways of combining intrinsic rewards, such as switching between the complementary forms.

How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty.

John Scalzi is a voluble man. He is the author of several New York Times best sellers and has been nominated for nearly every major award that the science-fiction industry has to offer--some of which he's won multiple times. Over the course of his career, he has written millions of words, filling dozens of books and 27 years' worth of posts on his personal blog. All of this is to say that if one wants to cite Scalzi, there is no shortage of material. But this month, the author noticed something odd: He was being quoted as saying things he'd never said.

Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules.

Causal traces show that the last token of the subject name is not always decisive.