In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non-linear regression on eleven real-world datasets, showing that it always outperforms GP regression and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks. As a by-product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks.

Output: Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Input: Consider Input: Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillation that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Furthermore, the same method can be used to consolidate multiple task-specific policies into a single policy. We demonstrate these claims using the Atari domain and show that the multi-task distilled agent outperforms the single-task teachers as well as a jointly-trained DQN agent.

Output: POLICY DISTILLATION

Input: Consider Input: The human intelligence lies in the algorithm, the nature of algorithms lies in the classification, and the classification is equal to outlier detection. This paper is based on its past unpublished edition (2009), which discussed an application of (pe) algorithm in outlier detection for time series data. The algorithm, also named as RDD algorithm, is originated from the study on general AI. United with it, designed modules can be used to realize kinds of tasks. A primary framework concerned with the mind through algorithm has been constructed in prior works. In this concise paper, we neglect background and minor description of the early edition, and directly discuss the main contents include longest kturn subsequence problem, curve type outliers, futural directions and related comments. In section "Past Present", we keep all prior conclusions, though a little might be out of date.
Output: Algorithm: its past present, futue present and comments