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]: We present a new unsupervised method for learning general-purpose sentence embeddings.
 Unlike existing methods which rely on local contexts, such as words
 inside the sentence or immediately neighboring sentences, our method selects, for
 each target sentence, influential sentences in the entire document based on a document
 structure. We identify a dependency structure of sentences using metadata
 or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
 technique to model many domain-specific terms, which were mostly discarded by
 existing sentence embedding methods. We validate our model on several tasks
 showing 30% precision improvement in coreference resolution in a technical domain,
 and 7.5% accuracy increase in paraphrase detection compared to baselines.
[A]: To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words.


[Q]: Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely on it. However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms. In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation. Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration. We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network. We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting. Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping. We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.
[A]: We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic.


[Q]: Neural networks have recently had a lot of success for many tasks. However, neural
 network architectures that perform well are still typically designed manually
 by experts in a cumbersome trial-and-error process. We propose a new method
 to automatically search for well-performing CNN architectures based on a simple
 hill climbing procedure whose operators apply network morphisms, followed
 by short optimization runs by cosine annealing. Surprisingly, this simple method
 yields competitive results, despite only requiring resources in the same order of
 magnitude as training a single network. E.g., on CIFAR-10, our method designs
 and trains networks with an error rate below 6% in only 12 hours on a single GPU;
 training for one day reduces this error further, to almost 5%.
[A]:
We propose a simple and efficent method for architecture search for convolutional neural networks.