Alibaba Group

About: Alibaba Group is a company organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Terminal (electronics). The organization has 6810 authors who have published 7389 publications receiving 55653 citations. The organization is also known as: Alibaba Group Holding Limited & Alibaba Group (Cayman Islands).

HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation

Abstract: Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, HoMM (order≥ 3) is expected to perform fine-grained domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at https://github.com/chenchao666/HoMM-Master

Multiple Object Tracking with Correlation Learning

Abstract: Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features. However, due to the local perception of the convolutional network structure itself, the long-range dependencies in both the spatial and temporal cannot be obtained efficiently. To incorporate the spatial layout, we propose to exploit the local correlation module to model the topological relationship between targets and their surrounding environment, which can enhance the discriminative power of our model in crowded scenes. Specifically, we establish dense correspondences of each spatial location and its context, and explicitly constrain the correlation volumes through self-supervised learning. To exploit the temporal context, existing approaches generally utilize two or more adjacent frames to construct an enhanced feature representation, but the dynamic motion scene is inherently difficult to depict via CNNs. Instead, our paper proposes a learnable correlation operator to establish frame-to-frame matches over convolutional feature maps in the different layers to align and propagate temporal context. With extensive experimental results on the MOT datasets, our approach demonstrates the effectiveness of correlation learning with the superior performance and obtains state-of-the-art MOTA of 76.5% and IDF1 of 73.6% on MOT17.

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

Abstract: A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations Particularly, we show that capsule networks improve multiple entity pairs relation extraction

Portfolio choices with orthogonal bandit learning

Abstract: The investigation and development of new methods from diverse perspectives to shed light on portfolio choice problems has never stagnated in financial research. Recently, multi-armed bandits have drawn intensive attention in various machine learning applications in online settings. The tradeoff between exploration and exploitation to maximize rewards in bandit algorithms naturally establishes a connection to portfolio choice problems. In this paper, we present a bandit algorithm for conducting online portfolio choices by effectually exploiting correlations among multiple arms. Through constructing orthogonal portfolios from multiple assets and integrating with the upper confidence bound bandit framework, we derive the optimal portfolio strategy that represents the combination of passive and active investments according to a risk-adjusted reward function. Compared with oft-quoted trading strategies in finance and machine learning fields across representative real-world market datasets, the proposed algorithm demonstrates superiority in both risk-adjusted return and cumulative wealth.

A Neural Multi-digraph Model for Chinese NER with Gazetteers

Abstract: Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.