Paper Group ANR 125
Locally Convex Sparse Learning over Networks. Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Spiking Deep Residual Network. Removing Algorithmic Discrimination (With M …
Locally Convex Sparse Learning over Networks
| Title | Locally Convex Sparse Learning over Networks |
| Authors | Ahmed Zaki, Saikat Chatterjee, Partha P. Mitra, Lars K. Rasmussen |
| Abstract | We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the network and reduce processing time. Each node of the network uses a convex optimization based algorithm that provides a locally optimum solution for that node. The nodes exchange their signal estimates over the network in order to refine their local estimates. At a node, the optimization algorithm is based on an $\ell_1$-norm minimization with appropriate modifications to promote sparsity as well as to include influence of estimates from neighboring nodes. Our expectation is that local estimates in each node improve fast and converge, resulting in a limited demand for communication of estimates between nodes and reducing the processing time. We provide restricted-isometry-property (RIP)-based theoretical analysis on estimation quality. In the scenario of clean observation, it is shown that the local estimates converge to the exact sparse signal under certain technical conditions. Simulation results show that the proposed algorithms show competitive performance compared to a globally optimum distributed LASSO algorithm in the sense of convergence speed and estimation error. |
| Tasks | Sparse Learning |
| Published | 2018-03-31 |
| URL | http://arxiv.org/abs/1804.00130v1 |
| http://arxiv.org/pdf/1804.00130v1.pdf | |
| PWC | https://paperswithcode.com/paper/locally-convex-sparse-learning-over-networks |
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Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate
| Title | Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate |
| Authors | Shen-Yi Zhao, Gong-Duo Zhang, Ming-Wei Li, Wu-Jun Li |
| Abstract | Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regularization. In this paper, we propose a novel method, called proximal \mbox{SCOPE}~(\mbox{pSCOPE}), for distributed sparse learning with $L_1$ regularization. pSCOPE is based on a \underline{c}ooperative \underline{a}utonomous \underline{l}ocal \underline{l}earning~(\mbox{CALL}) framework. In the \mbox{CALL} framework of \mbox{pSCOPE}, we find that the data partition affects the convergence of the learning procedure, and subsequently we define a metric to measure the goodness of a data partition. Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough. We also prove that better data partition implies faster convergence rate. Furthermore, pSCOPE is also communication efficient. Experimental results on real data sets show that pSCOPE can outperform other state-of-the-art distributed methods for sparse learning. |
| Tasks | Sparse Learning |
| Published | 2018-03-15 |
| URL | http://arxiv.org/abs/1803.05621v2 |
| http://arxiv.org/pdf/1803.05621v2.pdf | |
| PWC | https://paperswithcode.com/paper/proximal-scope-for-distributed-sparse |
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A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data
| Title | A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data |
| Authors | Yunchuan Kong, Tianwei Yu |
| Abstract | Gene expression data represents a unique challenge in predictive model building, because of the small number of samples $(n)$ compared to the huge amount of features $(p)$. This “$n«p$” property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (1) there are tens of thousands of features and only hundreds of training samples, (2) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method’s capability, we conducted both simulation experiments and a real data analysis using a breast cancer RNA-seq dataset from The Cancer Genome Atlas (TCGA). The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current classification models and feature selection procedures. The method is available at https://github.com/yunchuankong/NetworkNeuralNetwork. |
| Tasks | Feature Selection, Sparse Learning |
| Published | 2018-01-18 |
| URL | http://arxiv.org/abs/1801.06202v2 |
| http://arxiv.org/pdf/1801.06202v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-graph-embedded-deep-feedforward-network-for |
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Spiking Deep Residual Network
| Title | Spiking Deep Residual Network |
| Authors | Yangfan Hu, Huajin Tang, Yueming Wang, Gang Pan |
| Abstract | Recently, spiking neural network (SNN) has received significant attentions for its biological plausibility. SNN theoretically has at least the same computational power as traditional artificial neural networks (ANNs), and it has the potential to achieve revolutionary energy-efficiency. However, at current stage, it is still a big challenge to train a very deep SNN. In this paper, we propose an efficient approach to build a spiking version of deep residual network (ResNet), which represents the state-of-the-art convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named Spiking ResNet. To address the conversion problem, we propose a shortcut normalisation mechanism to appropriately scale continuous-valued activations to match firing rates in SNN, and a layer-wise error compensation approach to reduce the error caused by discretisation. Experimental results on MNIST, CIFAR-10, and CIFAR-100 demonstrate that the proposed Spiking ResNet yields the state-of-the-art performance of SNNs. |
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| Published | 2018-04-28 |
| URL | http://arxiv.org/abs/1805.01352v1 |
| http://arxiv.org/pdf/1805.01352v1.pdf | |
| PWC | https://paperswithcode.com/paper/spiking-deep-residual-network |
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Removing Algorithmic Discrimination (With Minimal Individual Error)
| Title | Removing Algorithmic Discrimination (With Minimal Individual Error) |
| Authors | El Mahdi El Mhamdi, Rachid Guerraoui, Lê Nguyên Hoang, Alexandre Maurer |
| Abstract | We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones where each population is a majority. We then address the general case of n populations, where the entanglement of populations does not allow a similar analytical solution. We show that an approximate solution with an arbitrarily high level of precision can be computed with linear programming. Finally, we address the inverse problem where the error should not go beyond a certain value and we seek to minimize the discrimination. |
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| Published | 2018-06-07 |
| URL | http://arxiv.org/abs/1806.02510v1 |
| http://arxiv.org/pdf/1806.02510v1.pdf | |
| PWC | https://paperswithcode.com/paper/removing-algorithmic-discrimination-with |
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Iterative multi-path tracking for video and volume segmentation with sparse point supervision
| Title | Iterative multi-path tracking for video and volume segmentation with sparse point supervision |
| Authors | Laurent Lejeune, Jan Grossrieder, Raphael Sznitman |
| Abstract | Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision. Our method relies on 2D point supervision, whereby a single 2D location within an object of interest is provided on each image of the data. Our method then estimates the object appearance in a semi-supervised fashion by learning object-image-specific features and by using these in a semi-supervised learning framework. Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation. In practice, we solve this optimally as a tracking problem using a K-shortest path approach. Both the object model and segmentation are then refined iteratively to further improve the final segmentation. We show that by collecting 2D locations using a gaze tracker, our approach can provide state-of-the-art segmentations on a range of objects and image modalities (video and 3D volumes), and that these can then be used to train supervised machine learning classifiers. |
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| Published | 2018-08-27 |
| URL | http://arxiv.org/abs/1809.00970v1 |
| http://arxiv.org/pdf/1809.00970v1.pdf | |
| PWC | https://paperswithcode.com/paper/iterative-multi-path-tracking-for-video-and |
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Deep Visual Odometry Methods for Mobile Robots
| Title | Deep Visual Odometry Methods for Mobile Robots |
| Authors | Jahanzaib Shabbir, Thomas Kruezer |
| Abstract | Technology has made navigation in 3D real time possible and this has made possible what seemed impossible. This paper explores the aspect of deep visual odometry methods for mobile robots. Visual odometry has been instrumental in making this navigation successful. Noticeable challenges in mobile robots including the inability to attain Simultaneous Localization and Mapping have been solved by visual odometry through its cameras which are suitable for human environments. More intuitive, precise and accurate detection have been made possible by visual odometry in mobile robots. Another challenge in the mobile robot world is the 3D map reconstruction for exploration. A dense map in mobile robots can facilitate for localization and more accurate findings. |
| Tasks | Simultaneous Localization and Mapping, Visual Odometry |
| Published | 2018-07-31 |
| URL | http://arxiv.org/abs/1807.11745v1 |
| http://arxiv.org/pdf/1807.11745v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-visual-odometry-methods-for-mobile |
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A Learning Framework for Robust Bin Picking by Customized Grippers
| Title | A Learning Framework for Robust Bin Picking by Customized Grippers |
| Authors | Yongxiang Fan, Hsien-Chung Lin, Te Tang, Masayoshi Tomizuka |
| Abstract | Customized grippers have specifically designed fingers to increase the contact area with the workpieces and improve the grasp robustness. However, grasp planning for customized grippers is challenging due to the object variations, surface contacts and structural constraints of the grippers. In this paper, we propose a learning framework to plan robust grasps for customized grippers in real-time. The learning framework contains a low-level optimization-based planner to search for optimal grasps locally under object shape variations, and a high-level learning-based explorer to learn the grasp exploration based on previous grasp experience. The optimization-based planner uses an iterative surface fitting (ISF) to simultaneously search for optimal gripper transformation and finger displacement by minimizing the surface fitting error. The high-level learning-based explorer trains a region-based convolutional neural network (R-CNN) to propose good optimization regions, which avoids ISF getting stuck in bad local optima and improves the collision avoidance performance. The proposed learning framework with RCNN-ISF is able to consider the structural constraints of the gripper, learn grasp exploration strategy from previous experience, and plan optimal grasps in clutter environment in real-time. The effectiveness of the algorithm is verified by experiments. |
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| Published | 2018-09-23 |
| URL | http://arxiv.org/abs/1809.08546v2 |
| http://arxiv.org/pdf/1809.08546v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-learning-framework-for-robust-bin-picking |
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Streaming Graph Neural Networks
| Title | Streaming Graph Neural Networks |
| Authors | Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin |
| Abstract | Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured data. These methods, which are usually known as the graph neural networks, have been applied to advance many graphs related tasks such as reasoning dynamics of the physical system, graph classification, and node classification. Most of the existing graph neural network models have been designed for static graphs, while many real-world graphs are inherently dynamic. For example, social networks are naturally evolving as new users joining and new relations being created. Current graph neural network models cannot utilize the dynamic information in dynamic graphs. However, the dynamic information has been proven to enhance the performance of many graph analytic tasks such as community detection and link prediction. Hence, it is necessary to design dedicated graph neural networks for dynamic graphs. In this paper, we propose DGNN, a new {\bf D}ynamic {\bf G}raph {\bf N}eural {\bf N}etwork model, which can model the dynamic information as the graph evolving. In particular, the proposed framework can keep updating node information by capturing the sequential information of edges (interactions), the time intervals between edges and information propagation coherently. Experimental results on various dynamic graphs demonstrate the effectiveness of the proposed framework. |
| Tasks | Community Detection, Graph Classification, Link Prediction, Node Classification |
| Published | 2018-10-24 |
| URL | http://arxiv.org/abs/1810.10627v2 |
| http://arxiv.org/pdf/1810.10627v2.pdf | |
| PWC | https://paperswithcode.com/paper/streaming-graph-neural-networks |
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Universal Marginalizer for Amortised Inference and Embedding of Generative Models
| Title | Universal Marginalizer for Amortised Inference and Embedding of Generative Models |
| Authors | Robert Walecki, Albert Buchard, Kostis Gourgoulias, Chris Hart, Maria Lomeli, A. K. W. Navarro, Max Zwiessele, Yura Perov, Saurabh Johri |
| Abstract | Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable number of methods for performing inference in probabilistic graphical models; however, they can be computationally costly due to significant time burden and/or storage requirements; or they lack theoretical guarantees of convergence and accuracy when applied to large scale graphical models. To this end, we propose the Universal Marginaliser Importance Sampler (UM-IS) – a hybrid inference scheme that combines the flexibility of a deep neural network trained on samples from the model and inherits the asymptotic guarantees of importance sampling. We show how combining samples drawn from the graphical model with an appropriate masking function allows us to train a single neural network to approximate any of the corresponding conditional marginal distributions, and thus amortise the cost of inference. We also show that the graph embeddings can be applied for tasks such as: clustering, classification and interpretation of relationships between the nodes. Finally, we benchmark the method on a large graph (>1000 nodes), showing that UM-IS outperforms sampling-based methods by a large margin while being computationally efficient. |
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| Published | 2018-11-12 |
| URL | http://arxiv.org/abs/1811.04727v1 |
| http://arxiv.org/pdf/1811.04727v1.pdf | |
| PWC | https://paperswithcode.com/paper/universal-marginalizer-for-amortised |
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Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning
| Title | Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning |
| Authors | Hanchen Xu, Alejandro D. Domínguez-García, Peter W. Sauer |
| Abstract | In this paper, we address the problem of setting the tap positions of load tap changers (LTCs) for voltage regulation in radial power distribution systems under uncertain load dynamics. The objective is to find a policy to determine the tap positions that only uses measurements of voltage magnitudes and topology information so as to minimize the voltage deviation across the system. We formulate this problem as a Markov decision process (MDP), and propose a batch reinforcement learning (RL) algorithm to solve it. By taking advantage of a linearized power flow model, we propose an effective algorithm to estimate the voltage magnitudes under different tap settings, which allows the RL algorithm to explore the state and action spaces freely offline without impacting the system operation. To circumvent the “curse of dimensionality” resulted from the large state and action spaces, we propose a sequential learning algorithm to learn an action-value function for each LTC, based on which the optimal tap positions can be directly determined. The effectiveness of the proposed algorithm is validated via numerical simulations on the IEEE 13-bus and 123-bus distribution test feeders. |
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| Published | 2018-07-29 |
| URL | http://arxiv.org/abs/1807.10997v2 |
| http://arxiv.org/pdf/1807.10997v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimal-tap-setting-of-voltage-regulation |
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Cascaded Pyramid Network for 3D Human Pose Estimation Challenge
| Title | Cascaded Pyramid Network for 3D Human Pose Estimation Challenge |
| Authors | Sungeun Hong, Wonjin Jung, Ilsang Woo, Seung Wook Kim |
| Abstract | Over the past decade, there has been a growing interest in human pose estimation. Although much work has been done on 2D pose estimation, 3D pose estimation has still been relatively studied less. In this paper, we propose a top-bottom based two-stage 3D estimation framework. GloabalNet and RefineNet in our 2D pose estimation process enable us to find occluded or invisible 2D joints while 2D-to-3D pose estimator composed of residual blocks is used to lift 2D joints to 3D joints effectively. The proposed method achieves promising results with mean per joint position error at 42.39 on the validation dataset on `3D Human Pose Estimation within the ECCV 2018 PoseTrack Challenge.’ | |
| Tasks | 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation |
| Published | 2018-10-03 |
| URL | http://arxiv.org/abs/1810.01616v1 |
| http://arxiv.org/pdf/1810.01616v1.pdf | |
| PWC | https://paperswithcode.com/paper/cascaded-pyramid-network-for-3d-human-pose |
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Tackling Sequence to Sequence Mapping Problems with Neural Networks
| Title | Tackling Sequence to Sequence Mapping Problems with Neural Networks |
| Authors | Lei Yu |
| Abstract | In Natural Language Processing (NLP), it is important to detect the relationship between two sequences or to generate a sequence of tokens given another observed sequence. We call the type of problems on modelling sequence pairs as sequence to sequence (seq2seq) mapping problems. A lot of research has been devoted to finding ways of tackling these problems, with traditional approaches relying on a combination of hand-crafted features, alignment models, segmentation heuristics, and external linguistic resources. Although great progress has been made, these traditional approaches suffer from various drawbacks, such as complicated pipeline, laborious feature engineering, and the difficulty for domain adaptation. Recently, neural networks emerged as a promising solution to many problems in NLP, speech recognition, and computer vision. Neural models are powerful because they can be trained end to end, generalise well to unseen examples, and the same framework can be easily adapted to a new domain. The aim of this thesis is to advance the state-of-the-art in seq2seq mapping problems with neural networks. We explore solutions from three major aspects: investigating neural models for representing sequences, modelling interactions between sequences, and using unpaired data to boost the performance of neural models. For each aspect, we propose novel models and evaluate their efficacy on various tasks of seq2seq mapping. |
| Tasks | Domain Adaptation, Feature Engineering, Speech Recognition |
| Published | 2018-10-25 |
| URL | http://arxiv.org/abs/1810.10802v1 |
| http://arxiv.org/pdf/1810.10802v1.pdf | |
| PWC | https://paperswithcode.com/paper/tackling-sequence-to-sequence-mapping |
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CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
| Title | CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning |
| Authors | Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, Pierre-Yves Oudeyer |
| Abstract | In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties. |
| Tasks | Multi-Goal Reinforcement Learning |
| Published | 2018-10-15 |
| URL | https://arxiv.org/abs/1810.06284v4 |
| https://arxiv.org/pdf/1810.06284v4.pdf | |
| PWC | https://paperswithcode.com/paper/curious-intrinsically-motivated-multi-task |
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Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization
| Title | Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization |
| Authors | Nils Gessert, Sarah Latus, Youssef S. Abdelwahed, David M. Leistner, Matthias Lutz, Alexander Schlaefer |
| Abstract | Bioresorbable scaffolds have become a popular choice for treatment of coronary heart disease, replacing traditional metal stents. Often, intravascular optical coherence tomography is used to assess potential malapposition after implantation and for follow-up examinations later on. Typically, the scaffold is manually reviewed by an expert, analyzing each of the hundreds of image slices. As this is time consuming, automatic stent detection and visualization approaches have been proposed, mostly for metal stent detection based on classic image processing. As bioresorbable scaffolds are harder to detect, recent approaches have used feature extraction and machine learning methods for automatic detection. However, these methods require detailed, pixel-level labels in each image slice and extensive feature engineering for the particular stent type which might limit the approaches’ generalization capabilities. Therefore, we propose a deep learning-based method for bioresorbable scaffold visualization using only image-level labels. A convolutional neural network is trained to predict whether an image slice contains a metal stent, a bioresorbable scaffold, or no device. Then, we derive local stent strut information by employing weakly supervised localization using saliency maps with guided backpropagation. As saliency maps are generally diffuse and noisy, we propose a novel patch-based method with image shifting which allows for high resolution stent visualization. Our convolutional neural network model achieves a classification accuracy of 99.0 % for image-level stent classification which can be used for both high quality in-slice stent visualization and 3D rendering of the stent structure. |
| Tasks | Feature Engineering |
| Published | 2018-10-22 |
| URL | http://arxiv.org/abs/1810.09578v1 |
| http://arxiv.org/pdf/1810.09578v1.pdf | |
| PWC | https://paperswithcode.com/paper/bioresorbable-scaffold-visualization-in-ivoct |
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