In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
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Question: It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we show how we can modify variational auto-encoders to perform this task. Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way. We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C’s). Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et al., 2017 and the BiVCCA method of Wang et al., 2016) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset (Liu et al., 2015).

Answer: A VAE-variant which can create diverse images corresponding to novel concrete or abstract "concepts" described using attribute vectors.


Question: What makes humans so good at solving seemingly complex video games?   Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play.

Answer: We investigate the various kinds of prior knowledge that help human learning and find that general priors about objects play the most critical role in guiding human gameplay.


Question: Network pruning has emerged as a powerful technique for reducing the size of deep neural networks. Pruning uncovers high-performance subnetworks by taking a trained dense network and gradually removing unimportant connections. Recently, alternative techniques have emerged for training sparse networks directly without having to train a large dense model beforehand, thereby achieving small memory footprints during both training and inference.These techniques are based on dynamic reallocation of non-zero parameters during training. Thus, they are in effect executing a training-time search for the optimal subnetwork. We investigate a most recent one of these techniques and conduct additional experiments to elucidate its behavior in training sparse deep convolutional networks. Dynamic parameter reallocation converges early during training to a highly trainable subnetwork. We show that neither the structure, nor the initialization of the discovered high-performance subnetwork is sufficient to explain its good performance. Rather, it is the dynamics of parameter reallocation that are responsible for successful learning. Dynamic parameter reallocation thus improves the trainability of deep convolutional networks, playing a similar role as overparameterization, without incurring the memory and computational cost of the latter.

Answer:
Dynamic parameter-reallocation enables the successful direct training of compact sparse networks, and it plays an indispensable role even when we know the optimal sparse network a-priori