Paper Group ANR 33
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games. An Objectness Score for Accurate and Fast Detection during Navigation. Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion. Machine Learning for AC Optimal Power Flow. Knowledge-guided Convolutional Ne …
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
| Title | Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games |
| Authors | Chun Kai Ling, Fei Fang, J. Zico Kolter |
| Abstract | With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions. Although a recent work provides a powerful differentiable end-to-end learning frameworks which embed a game solver within a deep-learning framework, allowing unknown game parameters to be learned via backpropagation, this framework faces significant limitations when applied to boundedly rational human agents and large scale problems, leading to poor practicality. In this paper, we address these limitations and propose a framework that is applicable for more practical settings. First, seeking to learn the rationality of human agents in complex two-player zero-sum games, we draw upon well-known ideas in decision theory to obtain a concise and interpretable agent behavior model, and derive solvers and gradients for end-to-end learning. Second, to scale up to large, real-world scenarios, we propose an efficient first-order primal-dual method which exploits the structure of extensive-form games, yielding significantly faster computation for both game solving and gradient computation. When tested on randomly generated games, we report speedups of orders of magnitude over previous approaches. We also demonstrate the effectiveness of our model on both real-world one-player settings and synthetic data. |
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| Published | 2019-03-11 |
| URL | http://arxiv.org/abs/1903.04101v1 |
| http://arxiv.org/pdf/1903.04101v1.pdf | |
| PWC | https://paperswithcode.com/paper/large-scale-learning-of-agent-rationality-in |
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An Objectness Score for Accurate and Fast Detection during Navigation
| Title | An Objectness Score for Accurate and Fast Detection during Navigation |
| Authors | Hongsun Choi, Mincheul Kang, Youngsun Kwon, Sung-eui Yoon |
| Abstract | We propose a novel method utilizing an objectness score for maintaining the locations and classes of objects detected from Mask R-CNN during mobile robot navigation. The objectness score is defined to measure how well the detector identifies the locations and classes of objects during navigation. Specifically, it is designed to increase when there is sufficient distance between a detected object and the camera. During the navigation process, we transform the locations of objects in 3D world coordinates into 2D image coordinates through an affine projection and decide whether to retain the classes of detected objects using the objectness score. We conducted experiments to determine how well the locations and classes of detected objects are maintained at various angles and positions. Experimental results showed that our approach is efficient and robust, regardless of changing angles and distances. |
| Tasks | Robot Navigation |
| Published | 2019-08-26 |
| URL | https://arxiv.org/abs/1909.05626v1 |
| https://arxiv.org/pdf/1909.05626v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-objectness-score-for-accurate-and-fast |
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Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion
| Title | Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion |
| Authors | Alexander H. Chang, Shiyu Feng, Yipu Zhao, Justin S. Smith, Patricio A. Vela |
| Abstract | Rectilinear forms of snake-like robotic locomotion are anticipated to be an advantage in obstacle-strewn scenarios characterizing urban disaster zones, subterranean collapses, and other natural environments. The elongated, laterally-narrow footprint associated with these motion strategies is well-suited to traversal of confined spaces and narrow pathways. Navigation and path planning in the absence of global sensing, however, remains a pivotal challenge to be addressed prior to practical deployment of these robotic mechanisms. Several challenges related to visual processing and localization need to be resolved to to enable navigation. As a first pass in this direction, we equip a wireless, monocular color camera to the head of a robotic snake. Visiual odometry and mapping from ORB-SLAM permits self-localization in planar, obstacle-strewn environments. Ground plane traversability segmentation in conjunction with perception-space collision detection permits path planning for navigation. A previously presented dynamical reduction of rectilinear snake locomotion to a non-holonomic kinematic vehicle informs both SLAM and planning. The simplified motion model is then applied to track planned trajectories through an obstacle configuration. This navigational framework enables a snake-like robotic platform to autonomously navigate and traverse unknown scenarios with only monocular vision. |
| Tasks | Robot Navigation |
| Published | 2019-08-19 |
| URL | https://arxiv.org/abs/1908.07101v1 |
| https://arxiv.org/pdf/1908.07101v1.pdf | |
| PWC | https://paperswithcode.com/paper/autonomous-monocular-vision-based-snake-robot |
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Machine Learning for AC Optimal Power Flow
| Title | Machine Learning for AC Optimal Power Flow |
| Authors | Neel Guha, Zhecheng Wang, Matt Wytock, Arun Majumdar |
| Abstract | We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids. |
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| Published | 2019-10-19 |
| URL | https://arxiv.org/abs/1910.08842v1 |
| https://arxiv.org/pdf/1910.08842v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-for-ac-optimal-power-flow |
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Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
| Title | Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction |
| Authors | Huiwei Zhou, Chengkun Lang, Zhuang Liu, Shixian Ning, Yingyu Lin, Lei Du |
| Abstract | Background: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs), which contain prior knowledge about chemicals and diseases. Prior knowledge provides strong support for CDR extraction. How to make full use of it is worth studying. Results: This paper proposes a novel model called “Knowledge-guided Convolutional Networks (KCN)” to leverage prior knowledge for CDR extraction. The proposed model first learns knowledge representations including entity embeddings and relation embeddings from KBs. Then, entity embeddings are used to control the propagation of context features towards a chemical-disease pair with gated convolutions. After that, relation embeddings are employed to further capture the weighted context features by a shared attention pooling. Finally, the weighted context features containing additional knowledge information are used for CDR extraction. Experiments on the BioCreative V CDR dataset show that the proposed KCN achieves 71.28% F1-score, which outperforms most of the state-of-the-art systems. Conclusions: This paper proposes a novel CDR extraction model KCN to make full use of prior knowledge. Experimental results demonstrate that KCN could effectively integrate prior knowledge and contexts for the performance improvement. |
| Tasks | Entity Embeddings, Relation Extraction |
| Published | 2019-12-23 |
| URL | https://arxiv.org/abs/1912.10590v1 |
| https://arxiv.org/pdf/1912.10590v1.pdf | |
| PWC | https://paperswithcode.com/paper/knowledge-guided-convolutional-networks-for |
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Fully Convolutional Search Heuristic Learning for Rapid Path Planners
| Title | Fully Convolutional Search Heuristic Learning for Rapid Path Planners |
| Authors | Yuka Ariki, Takuya Narihira |
| Abstract | Path-planning algorithms are an important part of a wide variety of robotic applications, such as mobile robot navigation and robot arm manipulation. However, in large search spaces in which local traps may exist, it remains challenging to reliably find a path while satisfying real-time constraints. Efforts to speed up the path search have led to the development of many practical path-planning algorithms. These algorithms often define a search heuristic to guide the search towards the goal. The heuristics should be carefully designed for each specific problem to ensure reliability in the various situations encountered in the problem. However, it is often difficult for humans to craft such robust heuristics, and the search performance often degrades under conditions that violate the heuristic assumption. Rather than manually designing the heuristics, in this work, we propose a learning approach to acquire these search heuristics. Our method represents the environment containing the obstacles as an image, and this image is fed into fully convolutional neural networks to produce a search heuristic image where every pixel represents a heuristic value (cost-to-go value to a goal) in the form of a vertex of a search graph. Training the heuristic is performed using previously collected planning results. Our preliminary experiments (2D grid world navigation experiments) demonstrate significant reduction in the search costs relative to a hand-designed heuristic. |
| Tasks | Robot Navigation |
| Published | 2019-08-09 |
| URL | https://arxiv.org/abs/1908.03343v1 |
| https://arxiv.org/pdf/1908.03343v1.pdf | |
| PWC | https://paperswithcode.com/paper/fully-convolutional-search-heuristic-learning |
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Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics
| Title | Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics |
| Authors | Nicholas Collins, Hanna Kurniawati |
| Abstract | We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics. The past decade has seen a substantial advancement in solving POMDP problems. However, constructing a suitable POMDP model remains difficult. Recently, neural network architectures have been proposed to alleviate the difficulty in acquiring such models. Although the results are promising, existing architectures restrict the type of system dynamics that can be learned –that is, system dynamics must be the same in all parts of the state space. TransNet relaxes such a restriction. Key to this relaxation is a novel neural network module that classifies the state space into classes and then learns the system dynamics of the different classes. TransNet uses this module together with the overall architecture of QMDP-Net[1] to allow solving POMDPs that have more expressive dynamic models, while maintaining efficient data requirement. Its evaluation on typical benchmarks in robot navigation with initially unknown system and environment models indicates that TransNet substantially out-performs the quality of the generated policies and learning efficiency of the state-of-the-art method QMDP-Net. |
| Tasks | Robot Navigation |
| Published | 2019-07-09 |
| URL | https://arxiv.org/abs/1907.04457v1 |
| https://arxiv.org/pdf/1907.04457v1.pdf | |
| PWC | https://paperswithcode.com/paper/partially-observable-planning-and-learning |
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Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification
| Title | Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification |
| Authors | Weinong Wang, Wenjie Pei, Qiong Cao, Shu Liu, Yu-Wing Tai |
| Abstract | Person re-identification aims to identify whether pairs of images belong to the same person or not. This problem is challenging due to large differences in camera views, lighting and background. One of the mainstream in learning CNN features is to design loss functions which reinforce both the class separation and intra-class compactness. In this paper, we propose a novel Orthogonal Center Learning method with Subspace Masking for person re-identification. We make the following contributions: (i) we develop a center learning module to learn the class centers by simultaneously reducing the intra-class differences and inter-class correlations by orthogonalization; (ii) we introduce a subspace masking mechanism to enhance the generalization of the learned class centers; and (iii) we devise to integrate the average pooling and max pooling in a regularizing manner that fully exploits their powers. Extensive experiments show that our proposed method consistently outperforms the state-of-the-art methods on the large-scale ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03 and MSMT17. |
| Tasks | Person Re-Identification |
| Published | 2019-08-28 |
| URL | https://arxiv.org/abs/1908.10535v2 |
| https://arxiv.org/pdf/1908.10535v2.pdf | |
| PWC | https://paperswithcode.com/paper/orthogonal-center-learning-with-subspace |
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A survey of Object Classification and Detection based on 2D/3D data
| Title | A survey of Object Classification and Detection based on 2D/3D data |
| Authors | Xiaoke Shen |
| Abstract | Recently, by using deep neural network based algorithms, object classification, detection and semantic segmentation solutions are significantly improved. However, one challenge for 2D image-based systems is that they cannot provide accurate 3D location information. This is critical for location sensitive applications such as autonomous driving and robot navigation. On the other hand, 3D methods, such as RGB-D and RGB-LiDAR based systems, can provide solutions that significantly improve the RGB only approaches. That is why this is an interesting research area for both industry and academia. Compared with 2D image-based systems, 3D-based systems are more complicated due to the following five reasons: 1) Data representation itself is more complicated. 3D images can be represented by point clouds, meshes, volumes. 2D images have pixel grid representations. 2) The computation and memory resource requirement is higher as an extra dimension is added. 3) Different distribution of the objects and difference in scene areas between indoor and outdoor make one unified framework hard to achieve. 4) 3D data, especially for the outdoor scenario, is sparse compared with the dense 2D images which makes the detection task more challenging. Finally, large size labelled datasets, which are extremely important for supervised based algorithms, are still under construction compared with well-built 2D datasets such as ImageNet. Based on challenges listed above, the described systems are organized by application scenarios, data representation methods and main tasks addressed. At the same time, critical 2D based systems which greatly influence the 3D ones are also introduced to show the connection between them. |
| Tasks | Autonomous Driving, Object Classification, Robot Navigation, Semantic Segmentation |
| Published | 2019-05-29 |
| URL | https://arxiv.org/abs/1905.12683v1 |
| https://arxiv.org/pdf/1905.12683v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-of-object-classification-and |
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Retrieval-based Goal-Oriented Dialogue Generation
| Title | Retrieval-based Goal-Oriented Dialogue Generation |
| Authors | Ana Valeria Gonzalez, Isabelle Augenstein, Anders Søgaard |
| Abstract | Most research on dialogue has focused either on dialogue generation for openended chit chat or on state tracking for goal-directed dialogue. In this work, we explore a hybrid approach to goal-oriented dialogue generation that combines retrieval from past history with a hierarchical, neural encoder-decoder architecture. We evaluate this approach in the customer support domain using the Multiwoz dataset (Budzianowski et al., 2018). We show that adding this retrieval step to a hierarchical, neural encoder-decoder architecture leads to significant improvements, including responses that are rated more appropriate and fluent by human evaluators. Finally, we compare our retrieval-based model to various semantically conditioned models explicitly using past dialog act information, and find that our proposed model is competitive with the current state of the art (Chen et al., 2019), while not requiring explicit labels about past machine acts. |
| Tasks | Dialogue Generation |
| Published | 2019-09-30 |
| URL | https://arxiv.org/abs/1909.13717v1 |
| https://arxiv.org/pdf/1909.13717v1.pdf | |
| PWC | https://paperswithcode.com/paper/retrieval-based-goal-oriented-dialogue |
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Parallel Contextual Bandits in Wireless Handover Optimization
| Title | Parallel Contextual Bandits in Wireless Handover Optimization |
| Authors | Igor Colin, Albert Thomas, Moez Draief |
| Abstract | As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to carefully adapt existing methods to fully leverage the multi-agent structure of this problem. We propose two approaches: one derived from a deterministic UCB-like method and the other relying on Thompson sampling. Thanks to its bayesian nature, the latter is intuited to better preserve the exploration-exploitation balance in the bandit batch. This is verified on toy experiments, where Thompson sampling shows robustness to the variability of the contexts. Finally, we apply both methods on a real base station network dataset and evidence that Thompson sampling outperforms both manual tuning and contextual UCB. |
| Tasks | Multi-Armed Bandits |
| Published | 2019-01-21 |
| URL | http://arxiv.org/abs/1902.01931v1 |
| http://arxiv.org/pdf/1902.01931v1.pdf | |
| PWC | https://paperswithcode.com/paper/parallel-contextual-bandits-in-wireless |
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From open learners to open games
| Title | From open learners to open games |
| Authors | Jules Hedges |
| Abstract | The categories of open learners (due to Fong, Spivak and Tuy'eras) and open games (due to the present author, Ghani, Winschel and Zahn) bear a very striking and unexpected similarity. The purpose of this short note is to prove that there is a faithful symmetric monoidal functor from the former to the latter, which means that any supervised neural network (without feedback or other complicating features) can be seen as an open game in a canonical way. Roughly, each parameter is controlled by a different player, and the game’s best response relation encodes the dynamics of gradient descent. We suggest paths for further work exploiting the link. |
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| Published | 2019-02-22 |
| URL | http://arxiv.org/abs/1902.08666v1 |
| http://arxiv.org/pdf/1902.08666v1.pdf | |
| PWC | https://paperswithcode.com/paper/from-open-learners-to-open-games |
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SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification
| Title | SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification |
| Authors | Yan Huang, Qiang Wu, JingSong Xu, Yi Zhong |
| Abstract | Cross-domain person re-identification (re-ID) is challenging due to the bias between training and testing domains. We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance. In this paper, we formulate such problems as a background shift problem. A Suppression of Background Shift Generative Adversarial Network (SBSGAN) is proposed to generate images with suppressed backgrounds. Unlike simply removing backgrounds using binary masks, SBSGAN allows the generator to decide whether pixels should be preserved or suppressed to reduce segmentation errors caused by noisy foreground masks. Additionally, we take ID-related cues, such as vehicles and companions into consideration. With high-quality generated images, a Densely Associated 2-Stream (DA-2S) network is introduced with Inter Stream Densely Connection (ISDC) modules to strengthen the complementarity of the generated data and ID-related cues. The experiments show that the proposed method achieves competitive performance on three re-ID datasets, ie., Market-1501, DukeMTMC-reID, and CUHK03, under the cross-domain person re-ID scenario. |
| Tasks | Person Re-Identification |
| Published | 2019-08-24 |
| URL | https://arxiv.org/abs/1908.09086v1 |
| https://arxiv.org/pdf/1908.09086v1.pdf | |
| PWC | https://paperswithcode.com/paper/sbsgan-suppression-of-inter-domain-background |
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Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
| Title | Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork |
| Authors | Yulong Wang, Xiaolin Hu, Hang Su |
| Abstract | We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure while maintaining comparable prediction performance. The structure representations of extracted subnetworks display a resemblance to their corresponding class semantic similarities. We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks. Experiments demonstrate that this intuitive operation can effectively improve explanation saliency accuracy for gradient-based explanation methods, and increase the detection rate for confidence score-based adversarial example detection methods. |
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| Published | 2019-10-07 |
| URL | https://arxiv.org/abs/1910.02673v1 |
| https://arxiv.org/pdf/1910.02673v1.pdf | |
| PWC | https://paperswithcode.com/paper/interpretable-disentanglement-of-neural |
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Evaluation of basic modules for isolated spelling error correction in Polish texts
| Title | Evaluation of basic modules for isolated spelling error correction in Polish texts |
| Authors | Szymon Rutkowski |
| Abstract | Spelling error correction is an important problem in natural language processing, as a prerequisite for good performance in downstream tasks as well as an important feature in user-facing applications. For texts in Polish language, there exist works on specific error correction solutions, often developed for dealing with specialized corpora, but not evaluations of many different approaches on big resources of errors. We begin to address this problem by testing some basic and promising methods on PlEWi, a corpus of annotated spelling extracted from Polish Wikipedia. These modules may be further combined with appropriate solutions for error detection and context awareness. Following our results, combining edit distance with cosine distance of semantic vectors may be suggested for interpretable systems, while an LSTM, particularly enhanced by ELMo embeddings, seems to offer the best raw performance. |
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| Published | 2019-05-26 |
| URL | https://arxiv.org/abs/1905.10810v1 |
| https://arxiv.org/pdf/1905.10810v1.pdf | |
| PWC | https://paperswithcode.com/paper/evaluation-of-basic-modules-for-isolated |
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