Paper Group ANR 449
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem. Distributed Averaging CNN-ELM for Big Data. A General Framework for Content-enhanced Network Representation Learning. Panoramic Structure from Motion via Geometric Relationship Detection. Explore First, Exploit Next: The True Shape of Regret in Bandit Probl …
On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem
| Title | On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem |
| Authors | Kevin Jamieson, Daniel Haas, Ben Recht |
| Abstract | This paper studies the trade-off between two different kinds of pure exploration: breadth versus depth. The most biased coin problem asks how many total coin flips are required to identify a “heavy” coin from an infinite bag containing both “heavy” coins with mean $\theta_1 \in (0,1)$, and “light” coins with mean $\theta_0 \in (0,\theta_1)$, where heavy coins are drawn from the bag with probability $\alpha \in (0,1/2)$. The key difficulty of this problem lies in distinguishing whether the two kinds of coins have very similar means, or whether heavy coins are just extremely rare. This problem has applications in crowdsourcing, anomaly detection, and radio spectrum search. Chandrasekaran et. al. (2014) recently introduced a solution to this problem but it required perfect knowledge of $\theta_0,\theta_1,\alpha$. In contrast, we derive algorithms that are adaptive to partial or absent knowledge of the problem parameters. Moreover, our techniques generalize beyond coins to more general instances of infinitely many armed bandit problems. We also prove lower bounds that show our algorithm’s upper bounds are tight up to $\log$ factors, and on the way characterize the sample complexity of differentiating between a single parametric distribution and a mixture of two such distributions. As a result, these bounds have surprising implications both for solutions to the most biased coin problem and for anomaly detection when only partial information about the parameters is known. |
| Tasks | Anomaly Detection |
| Published | 2016-03-25 |
| URL | http://arxiv.org/abs/1603.08037v1 |
| http://arxiv.org/pdf/1603.08037v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-detection-of-mixture-distributions |
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Distributed Averaging CNN-ELM for Big Data
| Title | Distributed Averaging CNN-ELM for Big Data |
| Authors | Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin |
| Abstract | Increasing the scalability of machine learning to handle big volume of data is a challenging task. The scale up approach has some limitations. In this paper, we proposed a scale out approach for CNN-ELM based on MapReduce on classifier level. Map process is the CNN-ELM training for certain partition of data. It involves many CNN-ELM models that can be trained asynchronously. Reduce process is the averaging of all CNN-ELM weights as final training result. This approach can save a lot of training time than single CNN-ELM models trained alone. This approach also increased the scalability of machine learning by combining scale out and scale up approaches. We verified our method in extended MNIST data set and not-MNIST data set experiment. However, it has some drawbacks by additional iteration learning parameters that need to be carefully taken and training data distribution that need to be carefully selected. Further researches to use more complex image data set are required. |
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| Published | 2016-10-07 |
| URL | http://arxiv.org/abs/1610.02373v1 |
| http://arxiv.org/pdf/1610.02373v1.pdf | |
| PWC | https://paperswithcode.com/paper/distributed-averaging-cnn-elm-for-big-data |
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A General Framework for Content-enhanced Network Representation Learning
| Title | A General Framework for Content-enhanced Network Representation Learning |
| Authors | Xiaofei Sun, Jiang Guo, Xiao Ding, Ting Liu |
| Abstract | This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods rely solely on the network structure, i.e., the linkage relationships between nodes, but ignore the rich content information associated with it, which is common in real world networks and beneficial to describing the characteristics of a node. In this paper, we propose content-enhanced network embedding (CENE), which is capable of jointly leveraging the network structure and the content information. Our approach integrates text modeling and structure modeling in a general framework by treating the content information as a special kind of node. Experiments on several real world net- works with application to node classification show that our models outperform all existing network embedding methods, demonstrating the merits of content information and joint learning. |
| Tasks | Network Embedding, Node Classification, Representation Learning |
| Published | 2016-10-10 |
| URL | http://arxiv.org/abs/1610.02906v3 |
| http://arxiv.org/pdf/1610.02906v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-general-framework-for-content-enhanced |
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Panoramic Structure from Motion via Geometric Relationship Detection
| Title | Panoramic Structure from Motion via Geometric Relationship Detection |
| Authors | Satoshi Ikehata, Ivaylo Boyadzhiev, Qi Shan, Yasutaka Furukawa |
| Abstract | This paper addresses the problem of Structure from Motion (SfM) for indoor panoramic image streams, extremely challenging even for the state-of-the-art due to the lack of textures and minimal parallax. The key idea is the fusion of single-view and multi-view reconstruction techniques via geometric relationship detection (e.g., detecting 2D lines as coplanar in 3D). Rough geometry suffices to perform such detection, and our approach utilizes rough surface normal estimates from an image-to-normal deep network to discover geometric relationships among lines. The detected relationships provide exact geometric constraints in our line-based linear SfM formulation. A constrained linear least squares is used to reconstruct a 3D model and camera motions, followed by the bundle adjustment. We have validated our algorithm on challenging datasets, outperforming various state-of-the-art reconstruction techniques. |
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| Published | 2016-12-05 |
| URL | http://arxiv.org/abs/1612.01256v1 |
| http://arxiv.org/pdf/1612.01256v1.pdf | |
| PWC | https://paperswithcode.com/paper/panoramic-structure-from-motion-via-geometric |
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Explore First, Exploit Next: The True Shape of Regret in Bandit Problems
| Title | Explore First, Exploit Next: The True Shape of Regret in Bandit Problems |
| Authors | Aurélien Garivier, Pierre Ménard, Gilles Stoltz |
| Abstract | We revisit lower bounds on the regret in the case of multi-armed bandit problems. We obtain non-asymptotic, distribution-dependent bounds and provide straightforward proofs based only on well-known properties of Kullback-Leibler divergences. These bounds show in particular that in an initial phase the regret grows almost linearly, and that the well-known logarithmic growth of the regret only holds in a final phase. The proof techniques come to the essence of the information-theoretic arguments used and they are deprived of all unnecessary complications. |
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| Published | 2016-02-23 |
| URL | http://arxiv.org/abs/1602.07182v3 |
| http://arxiv.org/pdf/1602.07182v3.pdf | |
| PWC | https://paperswithcode.com/paper/explore-first-exploit-next-the-true-shape-of |
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Incorporating Human Domain Knowledge into Large Scale Cost Function Learning
| Title | Incorporating Human Domain Knowledge into Large Scale Cost Function Learning |
| Authors | Markus Wulfmeier, Dushyant Rao, Ingmar Posner |
| Abstract | Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses. |
| Tasks | Motion Planning |
| Published | 2016-12-13 |
| URL | http://arxiv.org/abs/1612.04318v1 |
| http://arxiv.org/pdf/1612.04318v1.pdf | |
| PWC | https://paperswithcode.com/paper/incorporating-human-domain-knowledge-into |
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Incremental Sampling-based Motion Planners Using Policy Iteration Methods
| Title | Incremental Sampling-based Motion Planners Using Policy Iteration Methods |
| Authors | Oktay Arslan, Panagiotis Tsiotras |
| Abstract | Recent progress in randomized motion planners has led to the development of a new class of sampling-based algorithms that provide asymptotic optimality guarantees, notably the RRT* and the PRM* algorithms. Careful analysis reveals that the so-called “rewiring” step in these algorithms can be interpreted as a local policy iteration (PI) step (i.e., a local policy evaluation step followed by a local policy improvement step) so that asymptotically, as the number of samples tend to infinity, both algorithms converge to the optimal path almost surely (with probability 1). Policy iteration, along with value iteration (VI) are common methods for solving dynamic programming (DP) problems. Based on this observation, recently, the RRT$^{#}$ algorithm has been proposed, which performs, during each iteration, Bellman updates (aka “backups”) on those vertices of the graph that have the potential of being part of the optimal path (i.e., the “promising” vertices). The RRT$^{#}$ algorithm thus utilizes dynamic programming ideas and implements them incrementally on randomly generated graphs to obtain high quality solutions. In this work, and based on this key insight, we explore a different class of dynamic programming algorithms for solving shortest-path problems on random graphs generated by iterative sampling methods. These class of algorithms utilize policy iteration instead of value iteration, and thus are better suited for massive parallelization. Contrary to the RRT* algorithm, the policy improvement during the rewiring step is not performed only locally but rather on a set of vertices that are classified as “promising” during the current iteration. This tends to speed-up the whole process. The resulting algorithm, aptly named Policy Iteration-RRT$^{#}$ (PI-RRT$^{#}$) is the first of a new class of DP-inspired algorithms for randomized motion planning that utilize PI methods. |
| Tasks | Motion Planning |
| Published | 2016-09-19 |
| URL | http://arxiv.org/abs/1609.05960v1 |
| http://arxiv.org/pdf/1609.05960v1.pdf | |
| PWC | https://paperswithcode.com/paper/incremental-sampling-based-motion-planners |
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Causal Bandits: Learning Good Interventions via Causal Inference
| Title | Causal Bandits: Learning Good Interventions via Causal Inference |
| Authors | Finnian Lattimore, Tor Lattimore, Mark D. Reid |
| Abstract | We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that is strictly better (in all quantities) than algorithms that do not use the additional causal information. |
| Tasks | Causal Inference |
| Published | 2016-06-10 |
| URL | http://arxiv.org/abs/1606.03203v1 |
| http://arxiv.org/pdf/1606.03203v1.pdf | |
| PWC | https://paperswithcode.com/paper/causal-bandits-learning-good-interventions |
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An Adaptation of Topic Modeling to Sentences
| Title | An Adaptation of Topic Modeling to Sentences |
| Authors | Ruey-Cheng Chen, Reid Swanson, Andrew S. Gordon |
| Abstract | Advances in topic modeling have yielded effective methods for characterizing the latent semantics of textual data. However, applying standard topic modeling approaches to sentence-level tasks introduces a number of challenges. In this paper, we adapt the approach of latent-Dirichlet allocation to include an additional layer for incorporating information about the sentence boundaries in documents. We show that the addition of this minimal information of document structure improves the perplexity results of a trained model. |
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| Published | 2016-07-20 |
| URL | http://arxiv.org/abs/1607.05818v1 |
| http://arxiv.org/pdf/1607.05818v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-adaptation-of-topic-modeling-to-sentences |
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Faster decoding for subword level Phrase-based SMT between related languages
| Title | Faster decoding for subword level Phrase-based SMT between related languages |
| Authors | Anoop Kunchukuttan, Pushpak Bhattacharyya |
| Abstract | A common and effective way to train translation systems between related languages is to consider sub-word level basic units. However, this increases the length of the sentences resulting in increased decoding time. The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords. In a phrase-based SMT framework, we investigate different choices of decoder parameters as well as data format and their impact on decoding time and translation accuracy. We suggest best options for these settings that significantly improve decoding time with little impact on the translation accuracy. |
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| Published | 2016-11-01 |
| URL | http://arxiv.org/abs/1611.00354v1 |
| http://arxiv.org/pdf/1611.00354v1.pdf | |
| PWC | https://paperswithcode.com/paper/faster-decoding-for-subword-level-phrase |
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Counterfactual Reasoning about Intent for Interactive Navigation in Dynamic Environments
| Title | Counterfactual Reasoning about Intent for Interactive Navigation in Dynamic Environments |
| Authors | A. Bordallo, F. Previtali, N. Nardelli, S. Ramamoorthy |
| Abstract | Many modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning. This requires models that take into consideration the other agent’s intended actions in one’s own planning. We present a real-time motion planning framework that brings together a few key components including intention inference by reasoning counterfactually about potential motion of the other agents as they work towards different goals. By using a light-weight motion model, we achieve efficient iterative planning for fluid motion when avoiding pedestrians, in parallel with goal inference for longer range movement prediction. This inference framework is coupled with a novel distributed visual tracking method that provides reliable and robust models for the current belief-state of the monitored environment. This combined approach represents a computationally efficient alternative to previously studied policy learning methods that often require significant offline training or calibration and do not yet scale to densely populated environments. We validate this framework with experiments involving multi-robot and human-robot navigation. We further validate the tracker component separately on much larger scale unconstrained pedestrian data sets. |
| Tasks | Calibration, Decision Making, Motion Planning, Robot Navigation, Visual Tracking |
| Published | 2016-10-26 |
| URL | http://arxiv.org/abs/1610.08424v1 |
| http://arxiv.org/pdf/1610.08424v1.pdf | |
| PWC | https://paperswithcode.com/paper/counterfactual-reasoning-about-intent-for |
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Hierarchical Attention Network for Action Recognition in Videos
| Title | Hierarchical Attention Network for Action Recognition in Videos |
| Authors | Yilin Wang, Suhang Wang, Jiliang Tang, Neil O’Hare, Yi Chang, Baoxin Li |
| Abstract | Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i.e. it explicitly models the temporal transitions between frames as well as video segments and (3) with a multiple step spatial temporal attention mechanism, HAN automatically learns important regions in video frames and temporal segments in the video. The proposed model is trained and evaluated on the standard video action benchmarks, i.e., UCF-101 and HMDB-51, and it significantly outperforms the state-of-the arts |
| Tasks | Action Recognition In Videos, Temporal Action Localization |
| Published | 2016-07-21 |
| URL | http://arxiv.org/abs/1607.06416v1 |
| http://arxiv.org/pdf/1607.06416v1.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-attention-network-for-action |
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About Pyramid Structure in Convolutional Neural Networks
| Title | About Pyramid Structure in Convolutional Neural Networks |
| Authors | Ihsan Ullah, Alfredo Petrosino |
| Abstract | Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized statement over convolutional layers from input till fully connected layer is introduced that helps further in understanding and designing a successful deep network. It reduces ambiguity, number of parameters, and their size on disk without degrading overall accuracy. Performance are shown on state-of-the-art models for MNIST, Cifar-10, Cifar-100, and ImageNet-12 datasets. Despite more than 80% reduction in parameters for Caffe_LENET, challenging results are obtained. Further, despite 10-20% reduction in training data along with 10-40% reduction in parameters for AlexNet model and its variations, competitive results are achieved when compared to similar well-engineered deeper architectures. |
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| Published | 2016-08-14 |
| URL | http://arxiv.org/abs/1608.04064v1 |
| http://arxiv.org/pdf/1608.04064v1.pdf | |
| PWC | https://paperswithcode.com/paper/about-pyramid-structure-in-convolutional |
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A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization
| Title | A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization |
| Authors | Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro |
| Abstract | This paper considers consensus optimization problems where each node of a network has access to a different summand of an aggregate cost function. Nodes try to minimize the aggregate cost function, while they exchange information only with their neighbors. We modify the dual decomposition method to incorporate a curvature correction inspired by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method. The resulting dual D-BFGS method is a fully decentralized algorithm in which nodes approximate curvature information of themselves and their neighbors through the satisfaction of a secant condition. Dual D-BFGS is of interest in consensus optimization problems that are not well conditioned, making first order decentralized methods ineffective, and in which second order information is not readily available, making decentralized second order methods infeasible. Asynchronous implementation is discussed and convergence of D-BFGS is established formally for both synchronous and asynchronous implementations. Performance advantages relative to alternative decentralized algorithms are shown numerically. |
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| Published | 2016-03-23 |
| URL | http://arxiv.org/abs/1603.07195v1 |
| http://arxiv.org/pdf/1603.07195v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-decentralized-quasi-newton-method-for-dual |
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Control Matching via Discharge Code Sequences
| Title | Control Matching via Discharge Code Sequences |
| Authors | Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh |
| Abstract | In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients. Our approach first leverages on Word2Vec framework to embed ICD codes into vector-valued representation. We then propose a sequential algorithm for case-control matching on this representation space of diagnosis codes. The novel practice of applying the sequential matching on the vector representation lifted the matching accuracy measured through multiple clinical outcomes. We reported the results on a large-scale dataset to demonstrate the effectiveness of our method. For such a large dataset where most clinical information has been codified, the new method is particularly relevant. |
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| Published | 2016-12-02 |
| URL | http://arxiv.org/abs/1612.01812v1 |
| http://arxiv.org/pdf/1612.01812v1.pdf | |
| PWC | https://paperswithcode.com/paper/control-matching-via-discharge-code-sequences |
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