Paper Group ANR 1743
From Community to Role-based Graph Embeddings. Comparison of brain connectomes using geodesic distance on manifold:a twin study. Global Capacity Measures for Deep ReLU Networks via Path Sampling. Guided Attention Network for Object Detection and Counting on Drones. Tensor-Train Parameterization for Ultra Dimensionality Reduction. Automatic trajecto …
From Community to Role-based Graph Embeddings
| Title | From Community to Role-based Graph Embeddings |
| Authors | Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee |
| Abstract | Roles are sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities are sets of nodes with more connections inside the set than outside (based on proximity/closeness, density). Roles and communities are fundamentally different but important complementary notions. Recently, the notion of roles has become increasingly important and has gained a lot of attention due to the proliferation of work on learning representations (node/edge embeddings) from graphs that preserve the notion of roles. Unfortunately, recent work has sometimes confused the notion of roles and communities leading to misleading or incorrect claims about the capabilities of network embedding methods. As such, this manuscript seeks to clarify the differences between roles and communities, and formalize the general mechanisms (e.g., random walks, feature diffusion) that give rise to community or role-based embeddings. We show mathematically why embedding methods based on these identified mechanisms are either community or role-based. These mechanisms are typically easy to identify and can help researchers quickly determine whether a method is more prone to learn community or role-based embeddings. Furthermore, they also serve as a basis for developing new and better methods for community or role-based embeddings. Finally, we analyze and discuss the applications and data characteristics where community or role-based embeddings are most appropriate. |
| Tasks | Network Embedding |
| Published | 2019-08-22 |
| URL | https://arxiv.org/abs/1908.08572v1 |
| https://arxiv.org/pdf/1908.08572v1.pdf | |
| PWC | https://paperswithcode.com/paper/from-community-to-role-based-graph-embeddings |
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Comparison of brain connectomes using geodesic distance on manifold:a twin study
| Title | Comparison of brain connectomes using geodesic distance on manifold:a twin study |
| Authors | A. Yamin, M. Dayan, L. Squarcina, P. Brambilla, V. Murino, V. Diwadkar, D. Sona |
| Abstract | fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and task-based fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs to propose a novel approach for assessing similarity in functional networks. In particular, we compared network similarity between pairs of twins in task-relevant and task-orthogonal networks. The proposed method measures the similarity between functional networks using a geodesic distance between graph Laplacians. With method we show that networks are more similar in monozygotic twins compared to dizygotic twins. Furthermore, the similarity in monozygotic twins is higher for task-relevant, than task-orthogonal networks. |
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| Published | 2019-02-04 |
| URL | http://arxiv.org/abs/1902.01395v1 |
| http://arxiv.org/pdf/1902.01395v1.pdf | |
| PWC | https://paperswithcode.com/paper/comparison-of-brain-connectomes-using |
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Global Capacity Measures for Deep ReLU Networks via Path Sampling
| Title | Global Capacity Measures for Deep ReLU Networks via Path Sampling |
| Authors | Ryan Theisen, Jason M. Klusowski, Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher |
| Abstract | Classical results on the statistical complexity of linear models have commonly identified the norm of the weights $\w$ as a fundamental capacity measure. Generalizations of this measure to the setting of deep networks have been varied, though a frequently identified quantity is the product of weight norms of each layer. In this work, we show that for a large class of networks possessing a positive homogeneity property, similar bounds may be obtained instead in terms of the norm of the product of weights. Our proof technique generalizes a recently proposed sampling argument, which allows us to demonstrate the existence of sparse approximants of positive homogeneous networks. This yields covering number bounds, which can be converted to generalization bounds for multi-class classification that are comparable to, and in certain cases improve upon, existing results in the literature. Finally, we investigate our sampling procedure empirically, which yields results consistent with our theory. |
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| Published | 2019-10-22 |
| URL | https://arxiv.org/abs/1910.10245v1 |
| https://arxiv.org/pdf/1910.10245v1.pdf | |
| PWC | https://paperswithcode.com/paper/global-capacity-measures-for-deep-relu |
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Guided Attention Network for Object Detection and Counting on Drones
| Title | Guided Attention Network for Object Detection and Counting on Drones |
| Authors | Yuanqiang Cai, Dawei Du, Libo Zhang, Longyin Wen, Weiqiang Wang, Yanjun Wu, Siwei Lyu |
| Abstract | Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new Guided Attention Network (GANet) to deal with both object detection and counting tasks based on the feature pyramid. Different from the previous methods relying on unsupervised attention modules, we fuse different scales of feature maps by using the proposed weakly-supervised Background Attention (BA) between the background and objects for more semantic feature representation. Then, the Foreground Attention (FA) module is developed to consider both global and local appearance of the object to facilitate accurate localization. Moreover, the new data argumentation strategy is designed to train a robust model in various complex scenes. Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and PUCPR+) show the state-of-the-art detection and counting performance of the proposed method compared with existing methods. |
| Tasks | Object Detection |
| Published | 2019-09-25 |
| URL | https://arxiv.org/abs/1909.11307v1 |
| https://arxiv.org/pdf/1909.11307v1.pdf | |
| PWC | https://paperswithcode.com/paper/guided-attention-network-for-object-detection |
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Tensor-Train Parameterization for Ultra Dimensionality Reduction
| Title | Tensor-Train Parameterization for Ultra Dimensionality Reduction |
| Authors | Mingyuan Bai, S. T. Boris Choy, Xin Song, Junbin Gao |
| Abstract | Locality preserving projections (LPP) are a classical dimensionality reduction method based on data graph information. However, LPP is still responsive to extreme outliers. LPP aiming for vectorial data may undermine data structural information when it is applied to multidimensional data. Besides, it assumes the dimension of data to be smaller than the number of instances, which is not suitable for high-dimensional data. For high-dimensional data analysis, the tensor-train decomposition is proved to be able to efficiently and effectively capture the spatial relations. Thus, we propose a tensor-train parameterization for ultra dimensionality reduction (TTPUDR) in which the traditional LPP mapping is tensorized in terms of tensor-trains and the LPP objective is replaced with the Frobenius norm to increase the robustness of the model. The manifold optimization technique is utilized to solve the new model. The performance of TTPUDR is assessed on classification problems and TTPUDR significantly outperforms the past methods and the several state-of-the-art methods. |
| Tasks | Dimensionality Reduction |
| Published | 2019-08-14 |
| URL | https://arxiv.org/abs/1908.04924v1 |
| https://arxiv.org/pdf/1908.04924v1.pdf | |
| PWC | https://paperswithcode.com/paper/tensor-train-parameterization-for-ultra |
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Automatic trajectory measurement of large numbers of crowded objects
| Title | Automatic trajectory measurement of large numbers of crowded objects |
| Authors | Hui Li, Ye Liu, Yan Qiu Chen |
| Abstract | Complex motion patterns of natural systems, such as fish schools, bird flocks, and cell groups, have attracted great attention from scientists for years. Trajectory measurement of individuals is vital for quantitative and high-throughput study of their collective behaviors. However, such data are rare mainly due to the challenges of detection and tracking of large numbers of objects with similar visual features and frequent occlusions. We present an automatic and effective framework to measure trajectories of large numbers of crowded oval-shaped objects, such as fish and cells. We first use a novel dual ellipse locator to detect the coarse position of each individual and then propose a variance minimization active contour method to obtain the optimal segmentation results. For tracking, cost matrix of assignment between consecutive frames is trainable via a random forest classifier with many spatial, texture, and shape features. The optimal trajectories are found for the whole image sequence by solving two linear assignment problems. We evaluate the proposed method on many challenging data sets. |
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| Published | 2019-02-03 |
| URL | http://arxiv.org/abs/1902.00835v1 |
| http://arxiv.org/pdf/1902.00835v1.pdf | |
| PWC | https://paperswithcode.com/paper/automatic-trajectory-measurement-of-large |
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Open Event Extraction from Online Text using a Generative Adversarial Network
| Title | Open Event Extraction from Online Text using a Generative Adversarial Network |
| Authors | Rui Wang, Deyu Zhou, Yulan He |
| Abstract | To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15% is observed in F-measure. |
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| Published | 2019-08-25 |
| URL | https://arxiv.org/abs/1908.09246v1 |
| https://arxiv.org/pdf/1908.09246v1.pdf | |
| PWC | https://paperswithcode.com/paper/open-event-extraction-from-online-text-using |
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Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition
| Title | Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition |
| Authors | Jingxiao Zheng, Ruichi Yu, Jun-Cheng Chen, Boyu Lu, Carlos D. Castillo, Rama Chellappa |
| Abstract | Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from high-quality faces to low-quality ones through contextual connections, which are constructed based on context such as body appearance. However, previous methods have often propagated erroneous information due to lack of uncertainty modeling of the noisy contextual connections. In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph. UGG explicitly models the uncertainty of the contextual connections by adaptively updating the weights of the edge gates according to the identity distributions of the nodes during inference. UGG is a generic graphical model that can be applied at only inference time or with end-to-end training. We demonstrate the effectiveness of UGG with state-of-the-art results in the recently released challenging Cast Search in Movies and IARPA Janus Surveillance Video Benchmark dataset. |
| Tasks | Face Recognition |
| Published | 2019-05-07 |
| URL | https://arxiv.org/abs/1905.02756v2 |
| https://arxiv.org/pdf/1905.02756v2.pdf | |
| PWC | https://paperswithcode.com/paper/uncertainty-modeling-of-contextual-connection |
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An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays
| Title | An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays |
| Authors | Julian Zimmert, Yevgeny Seldin |
| Abstract | We propose a new algorithm for adversarial multi-armed bandits with unrestricted delays. The algorithm is based on a novel hybrid regularizer applied in the Follow the Regularized Leader (FTRL) framework. It achieves $\mathcal{O}(\sqrt{kn}+\sqrt{D\log(k)})$ regret guarantee, where $k$ is the number of arms, $n$ is the number of rounds, and $D$ is the total delay. The result matches the lower bound within constants and requires no prior knowledge of $n$ or $D$. Additionally, we propose a refined tuning of the algorithm, which achieves $\mathcal{O}(\sqrt{kn}+\min_{S}S+\sqrt{D_{\bar S}\log(k)})$ regret guarantee, where $S$ is a set of rounds excluded from delay counting, $\bar S = [n]\setminus S$ are the counted rounds, and $D_{\bar S}$ is the total delay in the counted rounds. If the delays are highly unbalanced, the latter regret guarantee can be significantly tighter than the former. The result requires no advance knowledge of the delays and resolves an open problem of Thune et al. (2019). The new FTRL algorithm and its refined tuning are anytime and require no doubling, which resolves another open problem of Thune et al. (2019). |
| Tasks | Multi-Armed Bandits |
| Published | 2019-10-14 |
| URL | https://arxiv.org/abs/1910.06054v1 |
| https://arxiv.org/pdf/1910.06054v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-optimal-algorithm-for-adversarial-bandits |
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Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE
| Title | Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE |
| Authors | Dennis Eschweiler, Johannes Stegmaier |
| Abstract | The presented algorithms for segmentation and tracking follow a 3-step approach where we detect, track and finally segment nuclei. In the preprocessing phase, we detect centroids of the cell nuclei using a convolutional neural network (CNN) for the 2D images and a Laplacian-of-Gaussian Scale Space Maximum Projection approach for the 3D data sets. Tracking was performed in a backwards fashion on the predicted seed points, i.e., starting at the last frame and sequentially connecting corresponding objects until the first frame was reached. Correspondences were identified by propagating detections of a frame t to its preceding frame t-1 and by combining redundant detections using a hierarchical clustering approach. The tracked centroids were then used as input to variants of the seeded watershed algorithm to obtain the final segmentation. |
| Tasks | Cell Segmentation |
| Published | 2019-04-15 |
| URL | http://arxiv.org/abs/1904.06890v1 |
| http://arxiv.org/pdf/1904.06890v1.pdf | |
| PWC | https://paperswithcode.com/paper/algorithms-used-for-the-cell-segmentation |
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Few Shot Speaker Recognition using Deep Neural Networks
| Title | Few Shot Speaker Recognition using Deep Neural Networks |
| Authors | Prashant Anand, Ajeet Kumar Singh, Siddharth Srivastava, Brejesh Lall |
| Abstract | The recent advances in deep learning are mostly driven by availability of large amount of training data. However, availability of such data is not always possible for specific tasks such as speaker recognition where collection of large amount of data is not possible in practical scenarios. Therefore, in this paper, we propose to identify speakers by learning from only a few training examples. To achieve this, we use a deep neural network with prototypical loss where the input to the network is a spectrogram. For output, we project the class feature vectors into a common embedding space, followed by classification. Further, we show the effectiveness of capsule net in a few shot learning setting. To this end, we utilize an auto-encoder to learn generalized feature embeddings from class-specific embeddings obtained from capsule network. We provide exhaustive experiments on publicly available datasets and competitive baselines, demonstrating the superiority and generalization ability of the proposed few shot learning pipelines. |
| Tasks | Few-Shot Learning, Speaker Recognition |
| Published | 2019-04-17 |
| URL | http://arxiv.org/abs/1904.08775v1 |
| http://arxiv.org/pdf/1904.08775v1.pdf | |
| PWC | https://paperswithcode.com/paper/few-shot-speaker-recognition-using-deep |
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Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images
| Title | Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images |
| Authors | Nils Gessert, Marcel Bengs, Lukas Wittig, Daniel Drömann, Tobias Keck, Alexander Schlaefer, David B. Ellebrecht |
| Abstract | Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback. Methods: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue. Results: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1 and in the colon, the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks. Conclusions: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible. |
| Tasks | Image Classification, Transfer Learning |
| Published | 2019-05-20 |
| URL | https://arxiv.org/abs/1905.07991v1 |
| https://arxiv.org/pdf/1905.07991v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-transfer-learning-methods-for-colon |
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Similarity Measures based on Local Game Trees
| Title | Similarity Measures based on Local Game Trees |
| Authors | Sabrina Evans, Paolo Turrini |
| Abstract | We study strategic similarity of game positions in two-player extensive games of perfect information, by looking at the structure of their local game trees, with the aim of improving the performance of game playing agents in detecting forcing continuations. We present a range of measures over the induced game trees and compare them against benchmark problems in chess, observing a promising level of accuracy in matching up trap states. |
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| Published | 2019-02-25 |
| URL | http://arxiv.org/abs/1902.09335v1 |
| http://arxiv.org/pdf/1902.09335v1.pdf | |
| PWC | https://paperswithcode.com/paper/similarity-measures-based-on-local-game-trees |
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Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds
| Title | Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds |
| Authors | Tassilo Klein, Moin Nabi |
| Abstract | Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime. |
| Tasks | Language Modelling, Question Answering, Question Generation |
| Published | 2019-11-06 |
| URL | https://arxiv.org/abs/1911.02365v1 |
| https://arxiv.org/pdf/1911.02365v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-answer-by-learning-to-ask-getting |
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Detecting Cyber-Related Discussions in Online Social Platforms
| Title | Detecting Cyber-Related Discussions in Online Social Platforms |
| Authors | Ruth Ikwu, Panos Louisvieris |
| Abstract | As the use of social platforms continues to evolve, in areas such as cyber-security and defence, it has become imperative to develop adaptive methods for tracking, identifying and investigating cyber-related activities on these platforms. This paper introduces a new approach for detecting cyber-related discussions in online social platforms using a candidate set of terms that are representative of the cyber domain. The objective of this paper is to create a cyber lexicon with cyber-related terms that is applicable to the automatic detection of cyber activities across various online platforms. The method presented in this paper applies natural language processing techniques to representative data from multiple social platform types such as Reddit, Stack overflow, twitter and cyberwar news to extract candidate terms for a generic cyber lexicon. In selecting the candidate terms, we introduce the APMIS Aggregated Pointwise Mutual Information Score in comparison with the Term Frequency-Term Degree Ratio (FDR Score) and Term Frequency-Inverse Document Frequency Score (TF-IDF Score). These scoring mechanisms are robust to account for term frequency, term relevance and mutual dependence between terms. Finally, we evaluate the performance of the cyber lexicon by measuring its precision of in classifying discussions as ‘Cyber-Related’ or ‘Non-Cyber-Related’. |
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| Published | 2019-07-04 |
| URL | https://arxiv.org/abs/1907.02383v1 |
| https://arxiv.org/pdf/1907.02383v1.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-cyber-related-discussions-in-online |
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