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.

Q: Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2 generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2 produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.

A: We show language understanding via reading is promising way to learn policies that generalise to new environments.
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Q: Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well. Bayesian neural networks, which learn distributions over the parameters of the network, are theoretically well-motivated by Bayesian principles, but do not perform as well as deep ensembles in practice, particularly under dataset shift. One possible explanation for this gap between theory and practice is that popular scalable approximate Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space. We investigate this hypothesis by building on recent work on understanding the loss landscape of neural networks and adding our own exploration to measure the similarity of functions in the space of predictions. Our results show that random initializations explore entirely different modes, while functions along an optimization trajectory or sampled from the subspace thereof cluster within a single mode predictions-wise, while often deviating significantly in the weight space. We demonstrate that while low-loss connectors between modes exist, they are not connected in the space of predictions. Developing the concept of the diversity--accuracy plane, we show that the decorrelation power of random initializations is unmatched by popular subspace sampling methods.

A: We study deep ensembles through the lens of loss landscape and the space of predictions, demonstrating that the decorrelation power of random initializations is unmatched by subspace sampling that only explores a single mode.
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Q: The fabrication of semiconductor involves etching process to remove selected areas from wafers. However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines. Traditional image processing usually demands on large number of annotated data and the performance is still poor. We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer. Then, we use simple image processing to carry out automatic measurement on the objects. We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset. We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments. Our deep learning based method demonstrates superiority over image processing approach with mean accuracy reaching over 96% for the measurements, compared with the ground truth. To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.

A:
Using deep learning method to carry out automatic measurement of SEM images in semiconductor industry
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