RUC AI Box 每周論文推薦 （10.8~10.14下）

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RUC AI Box 每周論文推薦（10.8~10.14上）

下面論文列表為本小組同學推薦和整理，關於論文細節請聯繫論文作者。

網路嵌入

Arbitrary-Order Proximity Preserved Network Embedding

#SIGKDD 2018

摘要：Network embedding has received increasing research attention in recent years. The existing methods show that the high-order proximity plays a key role in capturing the underlying structure of the network. However, two fundamental problems in preserving the high-order proximity remain unsolved. First, all the existing methods can only preserve fixed-order proximities, despite that proximities of different orders are often desired for distinct networks and target applications. Second, given a certain order proximity, the existing methods cannot guarantee accuracy and efficiency simultaneously. To address these challenges, we propose AROPE (arbitrary-order proximity preserved embedding), a novel network embedding method based on SVD framework. We theoretically prove the eigen-decomposition reweighting theorem, revealing the intrinsic relationship between proximities of different orders. With this theorem, we propose a scalable eigen-decomposition solution to derive the embedding vectors and shift them between proximities of arbitrary orders. Theoretical analysis is provided to guarantee that i) our method has a low marginal cost in shifting the embedding vectors across different orders, ii) given a certain order, our method can get the global optimal solutions, and iii) the overall time complexity of our method is linear with respect to network size. Extensive experimental results on several large-scale networks demonstrate that our proposed method greatly and consistently outperforms the baselines in various tasks including network reconstruction, link prediction and node classification.

推薦理由：該工作提出了提出了一種兼顧精準度與效率的網路嵌入方法AROPE。該方法能夠保留任意階的一致性，且能以較小代價將各階一致性相關聯。

SIDE: Representation Learning in Signed Directed Networks

#WWW 2018

摘要：Given a signed directed network, how can we learn node representations which fully encode structural information of the network including sign and direction of edges? Node representation learning or network embedding learns a mapping of each node to a vector. The mapping encodes structural information on network, providing low-dimensional dense node features for general machine learning and data mining frameworks. Since many social networks allow trust (friend) and distrust (enemy) relationships described by signed and directed edges, generalizing network embedding method to learn from sign and direction information in networks is crucial. In addition, social theories are critical tool in signed network analysis. However, none of the existing methods supports all of the desired properties: considering sign, direction, and social theoretical interpretation. In this paper, we propose SIDE, a general network embedding method that represents both sign and direction of edges in the embedding space. SIDE carefully formulates and optimizes likelihood over both direct and indirect signed connections. We provide socio-psychological interpretation for each component of likelihood function. We prove linear scalability of our algorithm and propose additional optimization techniques to reduce the training time and improve accuracy. Through extensive experiments on real-world signed directed networks, we show that SIDE effectively encodes structural information into the learned embedding.

推薦理由：SIDE刻畫了嵌入空間中邊的符號和方向的通用網路嵌入方法。闡述了直接相連及間接相連的可能性以及他們的社會心理解釋。

Deep Attributed Network Embedding

#IJCAI 2018

摘要：Network embedding has attracted a surge of attention in recent years. It is to learn the lowdimensional representation for nodes in a network,which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications.Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper,we propose a novel deep attributed network embedding approach, which can capture the high nonlinearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.

推薦理由：本文利用神經網路同時學習網路的拓撲結構和屬性信息，創新的技巧在於將結構信息和屬性信息統一，2種表示相互補充，保持在同一低維空間。

強化學習

Value Iteration Networks

#NIPS 2016 best paper

摘要：We introduce the value iteration network (VIN): a fully differentiable neural network with a 『planning module』 embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

推薦理由：強化學習作為一個序列決策問題，它連續選擇一些行為，從這些行為完成後得到最大的收益作為最好的結果，它的結果反饋是具有延時性的。文章中提出讓強化學習過程具有規劃能力的解決方案，引入一個輔助空間去提高模型泛化能力。

Robust Adversarial Reinforcement Learning

摘要：Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning is done in real world, the data scarcity leads to failed generalization from training to test scenarios (e.g., due to different friction or object masses). Inspired from H∞ control methods, we note that both modeling errors and differences in training and test scenarios can be viewed as extra forces/disturbances in the system. This paper proposes the idea of robust adversarial reinforcement learning (RARL), where we train an agent to operate in the presence of a destabilizing adversary that applies disturbance forces to the system. The jointly trained adversary is reinforced – that is, it learns an optimal destabilization policy. We formulate the policy learning as a zero-sum, minimax objective function. Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah, Swimmer, Hopper and Walker2d) conclusively demonstrate that our method (a) improves training stability; (b) is robust to differences in training/test conditions; and c) outperform the baseline even in the absence of the adversary

推薦理由：通過training一個對抗agent, 提出一種加強reinforcement learning 魯棒性的方法。

遷移學習

Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation

#AAAI 2018

摘要：Recently, domain adaptation based on deep models has been a promising way to deal with the domains with scarce labeled data, which is a critical problem for deep learning models. Domain adaptation propagates the knowledge from a source domain with rich information to the target domain. In reality, the source and target domains are mostly unbalanced in that the source domain is more resource-rich and thus has more reliable knowledge than the target domain. However, existing deep domain adaptation approaches often pre-assume the source and target domains balanced and equally, leading to a medium solution between the source and target domains, which is not optimal for the unbalanced domain adaptation. In this paper, we propose a novel Deep Asymmetric Transfer Network (DATN) to address the problem of unbalanced domain adaptation. Specifically, our model will learn a transfer function from the target domain to the source domain and meanwhile adapting the source domain classifier with more discriminative power to the target domain. By doing this, the deep model is able to adaptively put more emphasis on the resource-rich source domain. To alleviate the scarcity problem of supervised data, we further propose an unsupervised transfer method to propagate the knowledge from a lot of unsupervised data by minimizing the distribution discrepancy over the unlabeled data of two domains. The experiments on two real-world datasets demonstrate that DATN attains a substantial gain over state-of-the-art methods.

推薦理由：以往的深度領域適應模型把目標領域和源領域放在平等的位置，忽略了源領域與目標領域在數據量、信息量上的不平衡。該工作通過目標領域到源領域的遷移函數，在源領域對目標領域做調整，更傾向於利用源領域豐富的信息。此外，為了解決標籤稀少的問題，該工作提出了無監督的方法來減小無標籤數據上的分布差異（distribution discrepancy）。