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.

Adoption of deep learning in safety-critical systems raise the need for understanding what deep neural networks do not understand. Several methodologies to estimate model uncertainty have been proposed, but these methodologies constrain either how the neural network is trained or constructed. We present Outlier Detection In Neural networks (ODIN), an assumption-free method for detecting outlier observations during prediction, based on principles widely used in manufacturing process monitoring. By using a linear approximation of the hidden layer manifold, we add prediction-time outlier detection to models after training without altering architecture or training. We demonstrate that ODIN efficiently detect outliers during prediction on Fashion-MNIST, ImageNet-synsets and speech command recognition.
An add-on method for deep learning to detect outliers during prediction-time

We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer. Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set. Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as
 an ensemble of agents trained on the full data. We evaluate on the tasks of document retrieval and question answering. The
 improved performance seems due to the increased diversity of reformulation strategies. This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind. However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results. Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes. At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.
We use reinforcement learning for query reformulation on two tasks and surprisingly find that when training multiple agents diversity of the reformulations is more important than specialisation.

The conventional approach to solving the recommendation problem greedily ranks
 individual document candidates by prediction scores. However, this method fails to
 optimize the slate as a whole, and hence, often struggles to capture biases caused
 by the page layout and document interdepedencies. The slate recommendation
 problem aims to directly find the optimally ordered subset of documents (i.e.
 slates) that best serve users’ interests. Solving this problem is hard due to the
 combinatorial explosion of document candidates and their display positions on the
 page. Therefore we propose a paradigm shift from the traditional viewpoint of solving a ranking problem to a direct slate generation framework. In this paper, we introduce List Conditional Variational Auto-Encoders (ListCVAE),
 which learn the joint distribution of documents on the slate conditioned
 on user responses, and directly generate full slates. Experiments on simulated
 and real-world data show that List-CVAE outperforms greedy ranking methods
 consistently on various scales of documents corpora.

We used a CVAE type model structure to learn to directly generate slates/whole pages for recommendation systems.