May 6, 2019

3280 words 16 mins read

Paper Group ANR 276

DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM. Non-Local Color Image Denoising with Convolutional Neural Networks. A Discontinuous Neural Network for Non-Negative Sparse Approximation. Active Discriminative Text Representation Learning. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection …

DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM


Title	DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Authors	Feng Wang, Huichao Gong, Gaochao liu, Meijing Li, Chuangye Yan, Tian Xia, Xueming Li, Jianyang Zeng
Abstract	Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination.
Tasks
Published	2016-05-06
URL	http://arxiv.org/abs/1605.01838v1
PDF	http://arxiv.org/pdf/1605.01838v1.pdf
PWC	https://paperswithcode.com/paper/deeppicker-a-deep-learning-approach-for-fully
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Framework

Non-Local Color Image Denoising with Convolutional Neural Networks


Title	Non-Local Color Image Denoising with Convolutional Neural Networks
Authors	Stamatios Lefkimmiatis
Abstract	We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.
Tasks	Denoising, Image Denoising
Published	2016-11-21
URL	http://arxiv.org/abs/1611.06757v2
PDF	http://arxiv.org/pdf/1611.06757v2.pdf
PWC	https://paperswithcode.com/paper/non-local-color-image-denoising-with
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A Discontinuous Neural Network for Non-Negative Sparse Approximation


Title	A Discontinuous Neural Network for Non-Negative Sparse Approximation
Authors	Martijn Arts, Marius Cordts, Monika Gorin, Marc Spehr, Rudolf Mathar
Abstract	This paper investigates a discontinuous neural network which is used as a model of the mammalian olfactory system and can more generally be applied to solve non-negative sparse approximation problems. By inherently limiting the systems integrators to having non-negative outputs, the system function becomes discontinuous since the integrators switch between being inactive and being active. It is shown that the presented network converges to equilibrium points which are solutions to general non-negative least squares optimization problems. We specify a Caratheodory solution and prove that the network is stable, provided that the system matrix has full column-rank. Under a mild condition on the equilibrium point, we show that the network converges to its equilibrium within a finite number of switches. Two applications of the neural network are shown. Firstly, we apply the network as a model of the olfactory system and show that in principle it may be capable of performing complex sparse signal recovery tasks. Secondly, we generalize the application to include non-negative sparse approximation problems and compare the recovery performance to a classical non-negative basis pursuit denoising algorithm. We conclude that the recovery performance differs only marginally from the classical algorithm, while the neural network has the advantage that no performance critical regularization parameter has to be chosen prior to recovery.
Tasks	Denoising
Published	2016-03-21
URL	http://arxiv.org/abs/1603.06353v1
PDF	http://arxiv.org/pdf/1603.06353v1.pdf
PWC	https://paperswithcode.com/paper/a-discontinuous-neural-network-for-non
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Active Discriminative Text Representation Learning


Title	Active Discriminative Text Representation Learning
Authors	Ye Zhang, Matthew Lease, Byron C. Wallace
Abstract	We propose a new active learning (AL) method for text classification with convolutional neural networks (CNNs). In AL, one selects the instances to be manually labeled with the aim of maximizing model performance with minimal effort. Neural models capitalize on word embeddings as representations (features), tuning these to the task at hand. We argue that AL strategies for multi-layered neural models should focus on selecting instances that most affect the embedding space (i.e., induce discriminative word representations). This is in contrast to traditional AL approaches (e.g., entropy-based uncertainty sampling), which specify higher level objectives. We propose a simple approach for sentence classification that selects instances containing words whose embeddings are likely to be updated with the greatest magnitude, thereby rapidly learning discriminative, task-specific embeddings. We extend this approach to document classification by jointly considering: (1) the expected changes to the constituent word representations; and (2) the model’s current overall uncertainty regarding the instance. The relative emphasis placed on these criteria is governed by a stochastic process that favors selecting instances likely to improve representations at the outset of learning, and then shifts toward general uncertainty sampling as AL progresses. Empirical results show that our method outperforms baseline AL approaches on both sentence and document classification tasks. We also show that, as expected, the method quickly learns discriminative word embeddings. To the best of our knowledge, this is the first work on AL addressing neural models for text classification.
Tasks	Active Learning, Document Classification, Representation Learning, Sentence Classification, Text Classification, Word Embeddings
Published	2016-06-14
URL	http://arxiv.org/abs/1606.04212v4
PDF	http://arxiv.org/pdf/1606.04212v4.pdf
PWC	https://paperswithcode.com/paper/active-discriminative-text-representation
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GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection


Title	GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection
Authors	Christopher R. Harshaw, Robert A. Bridges, Michael D. Iannacone, Joel W. Reed, John R. Goodall
Abstract	This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets – small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.
Tasks	Anomaly Detection, Outlier Detection
Published	2016-02-02
URL	http://arxiv.org/abs/1602.01130v1
PDF	http://arxiv.org/pdf/1602.01130v1.pdf
PWC	https://paperswithcode.com/paper/graphprints-towards-a-graph-analytic-method
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Evolution of active categorical image classification via saccadic eye movement


Title	Evolution of active categorical image classification via saccadic eye movement
Authors	Randal S. Olson, Jason H. Moore, Christoph Adami
Abstract	Pattern recognition and classification is a central concern for modern information processing systems. In particular, one key challenge to image and video classification has been that the computational cost of image processing scales linearly with the number of pixels in the image or video. Here we present an intelligent machine (the “active categorical classifier,” or ACC) that is inspired by the saccadic movements of the eye, and is capable of classifying images by selectively scanning only a portion of the image. We harness evolutionary computation to optimize the ACC on the MNIST hand-written digit classification task, and provide a proof-of-concept that the ACC works on noisy multi-class data. We further analyze the ACC and demonstrate its ability to classify images after viewing only a fraction of the pixels, and provide insight on future research paths to further improve upon the ACC presented here.
Tasks	Image Classification, Video Classification
Published	2016-03-27
URL	http://arxiv.org/abs/1603.08233v2
PDF	http://arxiv.org/pdf/1603.08233v2.pdf
PWC	https://paperswithcode.com/paper/evolution-of-active-categorical-image
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Title	Modeling Trolling in Social Media Conversations
Authors	Luis Gerardo Mojica
Abstract	Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls’ and the responders’ perspectives, characterizing these two perspectives using four aspects, namely, the troll’s intention and his intention disclosure, as well as the responder’s interpretation of the troll’s intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. Finally, we identify the difficult-to-classify cases in our corpus and suggest potential solutions for them.
Tasks
Published	2016-12-15
URL	http://arxiv.org/abs/1612.05310v2
PDF	http://arxiv.org/pdf/1612.05310v2.pdf
PWC	https://paperswithcode.com/paper/modeling-trolling-in-social-media
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Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective Dataset of Paintings


Title	Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective Dataset of Paintings
Authors	Seyed Ali Amirshahi, Gregor Uwe Hayn-Leichsenring, Joachim Denzler, Christoph Redies
Abstract	Computational aesthetics is an emerging field of research which has attracted different research groups in the last few years. In this field, one of the main approaches to evaluate the aesthetic quality of paintings and photographs is a feature-based approach. Among the different features proposed to reach this goal, color plays an import role. In this paper, we introduce a novel dataset that consists of paintings of Western provenance from 36 well-known painters from the 15th to the 20th century. As a first step and to assess this dataset, using a classifier, we investigate the correlation between the subjective scores and two widely used features that are related to color perception and in different aesthetic quality assessment approaches. Results show a classification rate of up to 73% between the color features and the subjective scores.
Tasks
Published	2016-09-19
URL	http://arxiv.org/abs/1609.05583v1
PDF	http://arxiv.org/pdf/1609.05583v1.pdf
PWC	https://paperswithcode.com/paper/color-a-crucial-factor-for-aesthetic-quality
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Framework

Random projections of random manifolds


Title	Random projections of random manifolds
Authors	Subhaneil Lahiri, Peiran Gao, Surya Ganguli
Abstract	Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on how many projections are needed to accurately preserve the geometry of these manifolds, given their intrinsic dimensionality, volume and curvature. However, such works employ definitions of volume and curvature that are inherently difficult to compute. Therefore such theory cannot be easily tested against numerical simulations to understand the tightness of the proven bounds. We instead study typical distortions arising in random projections of an ensemble of smooth Gaussian random manifolds. We find explicitly computable, approximate theoretical bounds on the number of projections required to accurately preserve the geometry of these manifolds. Our bounds, while approximate, can only be violated with a probability that is exponentially small in the ambient dimension, and therefore they hold with high probability in cases of practical interest. Moreover, unlike previous work, we test our theoretical bounds against numerical experiments on the actual geometric distortions that typically occur for random projections of random smooth manifolds. We find our bounds are tighter than previous results by several orders of magnitude.
Tasks	Dimensionality Reduction
Published	2016-07-14
URL	http://arxiv.org/abs/1607.04331v2
PDF	http://arxiv.org/pdf/1607.04331v2.pdf
PWC	https://paperswithcode.com/paper/random-projections-of-random-manifolds
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Framework

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features


Title	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features
Authors	Zihao Chen, Luo Luo, Zhihua Zhang
Abstract	Recently, there has been an increasing interest in designing distributed convex optimization algorithms under the setting where the data matrix is partitioned on features. Algorithms under this setting sometimes have many advantages over those under the setting where data is partitioned on samples, especially when the number of features is huge. Therefore, it is important to understand the inherent limitations of these optimization problems. In this paper, with certain restrictions on the communication allowed in the procedures, we develop tight lower bounds on communication rounds for a broad class of non-incremental algorithms under this setting. We also provide a lower bound on communication rounds for a class of (randomized) incremental algorithms.
Tasks
Published	2016-12-02
URL	http://arxiv.org/abs/1612.00599v1
PDF	http://arxiv.org/pdf/1612.00599v1.pdf
PWC	https://paperswithcode.com/paper/communication-lower-bounds-for-distributed
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Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons


Title	Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons
Authors	Qiulei Dong, Zhanyi Hu
Abstract	Cadieu et al. (Cadieu,2014) reported that deep neural networks(DNNs) could rival the representation of primate inferotemporal cortex for object recognition. Lehky et al. (Lehky,2011) provided a statistical analysis on neural responses to object stimuli in primate AIT cortex. They found the intrinsic dimensionality of object representations in AIT cortex is around 100 (Lehky,2014). Considering the outstanding performance of DNNs in object recognition, it is worthwhile investigating whether the responses of DNN neurons have similar response statistics to those of AIT neurons. Following Lehky et al.‘s works, we analyze the response statistics to image stimuli and the intrinsic dimensionality of object representations of DNN neurons. Our findings show in terms of kurtosis and Pareto tail index, the response statistics on single-neuron selectivity and population sparseness of DNN neurons are fundamentally different from those of IT neurons except some special cases. By increasing the number of neurons and stimuli, the conclusions could alter substantially. In addition, with the ascendancy of the convolutional layers of DNNs, the single-neuron selectivity and population sparseness of DNN neurons increase, indicating the last convolutional layer is to learn features for object representations, while the following fully-connected layers are to learn categorization features. It is also found that a sufficiently large number of stimuli and neurons are necessary for obtaining a stable dimensionality. To our knowledge, this is the first work to analyze the response statistics of DNN neurons comparing with AIT neurons, and our results provide not only some insights into the discrepancy of DNN neurons with respect to IT neurons in object representation, but also shed some light on possible outcomes of IT neurons when the number of recorded neurons and stimuli is beyond the level in (Lehky,2011,2014).
Tasks	Object Recognition
Published	2016-12-12
URL	http://arxiv.org/abs/1612.03590v2
PDF	http://arxiv.org/pdf/1612.03590v2.pdf
PWC	https://paperswithcode.com/paper/statistics-of-visual-responses-to-object
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Framework

Complex-Valued Kernel Methods for Regression


Title	Complex-Valued Kernel Methods for Regression
Authors	Rafael Boloix-Tortosa, Juan José Murillo-Fuentes, Irene Santos Velázquez, Fernando Pérez-Cruz
Abstract	Usually, complex-valued RKHS are presented as an straightforward application of the real-valued case. In this paper we prove that this procedure yields a limited solution for regression. We show that another kernel, here denoted as pseudo kernel, is needed to learn any function in complex-valued fields. Accordingly, we derive a novel RKHS to include it, the widely RKHS (WRKHS). When the pseudo-kernel cancels, WRKHS reduces to complex-valued RKHS of previous approaches. We address the kernel and pseudo-kernel design, paying attention to the kernel and the pseudo-kernel being complex-valued. In the experiments included we report remarkable improvements in simple scenarios where real a imaginary parts have different similitude relations for given inputs or cases where real and imaginary parts are correlated. In the context of these novel results we revisit the problem of non-linear channel equalization, to show that the WRKHS helps to design more efficient solutions.
Tasks
Published	2016-10-31
URL	http://arxiv.org/abs/1610.09915v1
PDF	http://arxiv.org/pdf/1610.09915v1.pdf
PWC	https://paperswithcode.com/paper/complex-valued-kernel-methods-for-regression
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Nonlinear Hebbian learning as a unifying principle in receptive field formation


Title	Nonlinear Hebbian learning as a unifying principle in receptive field formation
Authors	Carlos S. N. Brito, Wulfram Gerstner
Abstract	The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.
Tasks
Published	2016-01-04
URL	http://arxiv.org/abs/1601.00701v1
PDF	http://arxiv.org/pdf/1601.00701v1.pdf
PWC	https://paperswithcode.com/paper/nonlinear-hebbian-learning-as-a-unifying
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Framework

One-Shot Session Recommendation Systems with Combinatorial Items


Title	One-Shot Session Recommendation Systems with Combinatorial Items
Authors	Yahel David, Dotan Di Castro, Zohar Karnin
Abstract	In recent years, content recommendation systems in large websites (or \emph{content providers}) capture an increased focus. While the type of content varies, e.g.\ movies, articles, music, advertisements, etc., the high level problem remains the same. Based on knowledge obtained so far on the user, recommend the most desired content. In this paper we present a method to handle the well known user-cold-start problem in recommendation systems. In this scenario, a recommendation system encounters a new user and the objective is to present items as relevant as possible with the hope of keeping the user’s session as long as possible. We formulate an optimization problem aimed to maximize the length of this initial session, as this is believed to be the key to have the user come back and perhaps register to the system. In particular, our model captures the fact that a single round with low quality recommendation is likely to terminate the session. In such a case, we do not proceed to the next round as the user leaves the system, possibly never to seen again. We denote this phenomenon a \emph{One-Shot Session}. Our optimization problem is formulated as an MDP where the action space is of a combinatorial nature as we recommend in each round, multiple items. This huge action space presents a computational challenge making the straightforward solution intractable. We analyze the structure of the MDP to prove monotone and submodular like properties that allow a computationally efficient solution via a method denoted by \emph{Greedy Value Iteration} (G-VI).
Tasks	Recommendation Systems
Published	2016-07-05
URL	http://arxiv.org/abs/1607.01381v1
PDF	http://arxiv.org/pdf/1607.01381v1.pdf
PWC	https://paperswithcode.com/paper/one-shot-session-recommendation-systems-with
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Framework

Automatic Separation of Compound Figures in Scientific Articles


Title	Automatic Separation of Compound Figures in Scientific Articles
Authors	Mario Taschwer, Oge Marques
Abstract	Content-based analysis and retrieval of digital images found in scientific articles is often hindered by images consisting of multiple subfigures (compound figures). We address this problem by proposing a method to automatically classify and separate compound figures, which consists of two main steps: (i) a supervised compound figure classifier (CFC) discriminates between compound and non-compound figures using task-specific image features; and (ii) an image processing algorithm is applied to predicted compound images to perform compound figure separation (CFS). Our CFC approach is shown to achieve state-of-the-art classification performance on a published dataset. Our CFS algorithm shows superior separation accuracy on two different datasets compared to other known automatic approaches. Finally, we propose a method to evaluate the effectiveness of the CFC-CFS process chain and use it to optimize the misclassification loss of CFC for maximal effectiveness in the process chain.
Tasks
Published	2016-06-03
URL	http://arxiv.org/abs/1606.01021v2
PDF	http://arxiv.org/pdf/1606.01021v2.pdf
PWC	https://paperswithcode.com/paper/automatic-separation-of-compound-figures-in
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