Paper Group ANR 80
Covariant Compositional Networks For Learning Graphs. Generalized Bregman and Jensen divergences which include some f-divergences. Predicting Opioid Relapse Using Social Media Data. Deep Nets: What have they ever done for Vision?. Learning Sampling Policies for Domain Adaptation. Strategies for Training Stain Invariant CNNs. Three Efficient, Low-Co …
Covariant Compositional Networks For Learning Graphs
| Title | Covariant Compositional Networks For Learning Graphs |
| Authors | Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi |
| Abstract | Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that this imposes a limitation on their representation power, and instead propose a new general architecture for representing objects consisting of a hierarchy of parts, which we call Covariant Compositional Networks (CCNs). Here, covariance means that the activation of each neuron must transform in a specific way under permutations, similarly to steerability in CNNs. We achieve covariance by making each activation transform according to a tensor representation of the permutation group, and derive the corresponding tensor aggregation rules that each neuron must implement. Experiments show that CCNs can outperform competing methods on standard graph learning benchmarks. |
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| Published | 2018-01-07 |
| URL | http://arxiv.org/abs/1801.02144v1 |
| http://arxiv.org/pdf/1801.02144v1.pdf | |
| PWC | https://paperswithcode.com/paper/covariant-compositional-networks-for-learning |
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Generalized Bregman and Jensen divergences which include some f-divergences
| Title | Generalized Bregman and Jensen divergences which include some f-divergences |
| Authors | Tomohiro Nishiyama |
| Abstract | In this paper, we introduce new classes of divergences by extending the definitions of the Bregman divergence and the skew Jensen divergence. These new divergence classes (g-Bregman divergence and skew g-Jensen divergence) satisfy some properties similar to the Bregman or skew Jensen divergence. We show these g-divergences include divergences which belong to a class of f-divergence (the Hellinger distance, the chi-square divergence and the alpha-divergence in addition to the Kullback-Leibler divergence). Moreover, we derive an inequality between the g-Bregman divergence and the skew g-Jensen divergence and show this inequality is a generalization of Lin’s inequality. |
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| Published | 2018-08-19 |
| URL | http://arxiv.org/abs/1808.06148v5 |
| http://arxiv.org/pdf/1808.06148v5.pdf | |
| PWC | https://paperswithcode.com/paper/generalized-bregman-and-jensen-divergences |
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Predicting Opioid Relapse Using Social Media Data
| Title | Predicting Opioid Relapse Using Social Media Data |
| Authors | Zhou Yang, Long Nguyen, Fang Jin |
| Abstract | Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions joy' and negative’. This work is one of the first attempts to predict relapses using massive social media data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment. |
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| Published | 2018-11-14 |
| URL | https://arxiv.org/abs/1811.12169v1 |
| https://arxiv.org/pdf/1811.12169v1.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-opioid-relapse-using-social-media |
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Deep Nets: What have they ever done for Vision?
| Title | Deep Nets: What have they ever done for Vision? |
| Authors | Alan L. Yuille, Chenxi Liu |
| Abstract | This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the center of recent progress on artificial intelligence and are of growing importance in cognitive science and neuroscience. They have enormous successes but also clear limitations. There is also only partial understanding of their inner workings. It seems unlikely that Deep Nets in their current form will be the best long-term solution either for building general purpose intelligent machines or for understanding the mind/brain, but it is likely that many aspects of them will remain. At present Deep Nets do very well on specific types of visual tasks and on specific benchmarked datasets. But Deep Nets are much less general purpose, flexible, and adaptive than the human visual system. Moreover, methods like Deep Nets may run into fundamental difficulties when faced with the enormous complexity of natural images which can lead to a combinatorial explosion. To illustrate our main points, while keeping the references small, this paper is slightly biased towards work from our group. |
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| Published | 2018-05-10 |
| URL | http://arxiv.org/abs/1805.04025v2 |
| http://arxiv.org/pdf/1805.04025v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-nets-what-have-they-ever-done-for-vision |
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Learning Sampling Policies for Domain Adaptation
| Title | Learning Sampling Policies for Domain Adaptation |
| Authors | Yash Patel, Kashyap Chitta, Bhavan Jasani |
| Abstract | We address the problem of semi-supervised domain adaptation of classification algorithms through deep Q-learning. The core idea is to consider the predictions of a source domain network on target domain data as noisy labels, and learn a policy to sample from this data so as to maximize classification accuracy on a small annotated reward partition of the target domain. Our experiments show that learned sampling policies construct labeled sets that improve accuracies of visual classifiers over baselines. |
| Tasks | Domain Adaptation, Q-Learning |
| Published | 2018-05-19 |
| URL | http://arxiv.org/abs/1805.07641v1 |
| http://arxiv.org/pdf/1805.07641v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-sampling-policies-for-domain |
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Strategies for Training Stain Invariant CNNs
| Title | Strategies for Training Stain Invariant CNNs |
| Authors | Thomas Lampert, Odyssée Merveille, Jessica Schmitz, Germain Forestier, Friedrich Feuerhake, Cédric Wemmert |
| Abstract | An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive sections containing corresponding microscopic structures on glass slides, and to stain them differently to highlight specific tissue components. These multiple staining modalities result in very different images but include a significant amount of consistent image information. Deep learning approaches have recently been proposed to analyse these images in order to automatically identify objects of interest for pathologists. These supervised approaches require a vast amount of annotations, which are difficult and expensive to acquire—a problem that is multiplied with multiple stainings. This article presents several training strategies that make progress towards stain invariant networks. By training the network on one commonly used staining modality and applying it to images that include corresponding but differently stained tissue structures, the presented unsupervised strategies demonstrate significant improvements over standard training strategies. |
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| Published | 2018-10-17 |
| URL | http://arxiv.org/abs/1810.10338v2 |
| http://arxiv.org/pdf/1810.10338v2.pdf | |
| PWC | https://paperswithcode.com/paper/strategies-for-training-stain-invariant-cnns |
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Three Efficient, Low-Complexity Algorithms for Automatic Color Trapping
| Title | Three Efficient, Low-Complexity Algorithms for Automatic Color Trapping |
| Authors | Haiyin Wang, Mireille Boutin, Jeffrey Trask, Jan Allebach |
| Abstract | Color separations (most often cyan, magenta, yellow, and black) are commonly used in printing to reproduce multi-color images. For mechanical reasons, these color separations are generally not perfectly aligned with respect to each other when they are rendered by their respective imaging stations. This phenomenon, called color plane misregistration, causes gap and halo artifacts in the printed image. Color trapping is an image processing technique that aims to reduce these artifacts by modifying the susceptible edge boundaries to create small, unnoticeable overlaps between the color planes. We propose three low-complexity algorithms for automatic color trapping which hide the effects of small color plane mis-registrations. Our algorithms are designed for software or embedded firmware implementation. The trapping method they follow is based on a hardware-friendly technique proposed by J. Trask (JTHBCT03) which is too computationally expensive for software or firmware implementation. The first two algorithms are based on the use of look-up tables (LUTs). The first LUT-based algorithm corrects all registration errors of one pixel in extent and reduces several cases of misregistration errors of two pixels in extent using only 727 Kbytes of storage space. This algorithm is particularly attractive for implementation in the embedded firmware of low-cost formatter-based printers. The second LUT-based algorithm corrects all types of misregistration errors of up to two pixels in extent using 3.7 Mbytes of storage space. The third algorithm is a hybrid one that combines LUTs and feature extraction to minimize the storage requirements (724 Kbytes) while still correcting all misregistration errors of up to two pixels in extent. This algorithm is suitable for both embedded firmware implementation on low-cost formatter-based printers and software implementation on host-based printers. |
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| Published | 2018-08-21 |
| URL | http://arxiv.org/abs/1808.07096v1 |
| http://arxiv.org/pdf/1808.07096v1.pdf | |
| PWC | https://paperswithcode.com/paper/three-efficient-low-complexity-algorithms-for |
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Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
| Title | Synthesizing realistic neural population activity patterns using Generative Adversarial Networks |
| Authors | Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, Stefano Panzeri |
| Abstract | The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain. We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics. We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches. Finally, we show how to exploit a trained Spike-GAN to construct ‘importance maps’ to detect the most relevant statistical structures present in a spike train. Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience. |
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| Published | 2018-03-01 |
| URL | http://arxiv.org/abs/1803.00338v2 |
| http://arxiv.org/pdf/1803.00338v2.pdf | |
| PWC | https://paperswithcode.com/paper/synthesizing-realistic-neural-population |
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Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
| Title | Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality |
| Authors | Taiji Suzuki |
| Abstract | Deep learning has shown high performances in various types of tasks from visual recognition to natural language processing, which indicates superior flexibility and adaptivity of deep learning. To understand this phenomenon theoretically, we develop a new approximation and estimation error analysis of deep learning with the ReLU activation for functions in a Besov space and its variant with mixed smoothness. The Besov space is a considerably general function space including the Holder space and Sobolev space, and especially can capture spatial inhomogeneity of smoothness. Through the analysis in the Besov space, it is shown that deep learning can achieve the minimax optimal rate and outperform any non-adaptive (linear) estimator such as kernel ridge regression, which shows that deep learning has higher adaptivity to the spatial inhomogeneity of the target function than other estimators such as linear ones. In addition to this, it is shown that deep learning can avoid the curse of dimensionality if the target function is in a mixed smooth Besov space. We also show that the dependency of the convergence rate on the dimensionality is tight due to its minimax optimality. These results support high adaptivity of deep learning and its superior ability as a feature extractor. |
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| Published | 2018-10-18 |
| URL | http://arxiv.org/abs/1810.08033v1 |
| http://arxiv.org/pdf/1810.08033v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptivity-of-deep-relu-network-for-learning |
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Offline Evaluation of Ranking Policies with Click Models
| Title | Offline Evaluation of Ranking Policies with Click Models |
| Authors | Shuai Li, Yasin Abbasi-Yadkori, Branislav Kveton, S. Muthukrishnan, Vishwa Vinay, Zheng Wen |
| Abstract | Many web systems rank and present a list of items to users, from recommender systems to search and advertising. An important problem in practice is to evaluate new ranking policies offline and optimize them before they are deployed. We address this problem by proposing evaluation algorithms for estimating the expected number of clicks on ranked lists from historical logged data. The existing algorithms are not guaranteed to be statistically efficient in our problem because the number of recommended lists can grow exponentially with their length. To overcome this challenge, we use models of user interaction with the list of items, the so-called click models, to construct estimators that learn statistically efficiently. We analyze our estimators and prove that they are more efficient than the estimators that do not use the structure of the click model, under the assumption that the click model holds. We evaluate our estimators in a series of experiments on a real-world dataset and show that they consistently outperform prior estimators. |
| Tasks | Recommendation Systems |
| Published | 2018-04-27 |
| URL | http://arxiv.org/abs/1804.10488v2 |
| http://arxiv.org/pdf/1804.10488v2.pdf | |
| PWC | https://paperswithcode.com/paper/offline-evaluation-of-ranking-policies-with |
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MOVI: A Model-Free Approach to Dynamic Fleet Management
| Title | MOVI: A Model-Free Approach to Dynamic Fleet Management |
| Authors | Takuma Oda, Carlee Joe-Wong |
| Abstract | Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers’ waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of uncertainty, including vehicles’ travel times to their dispatched locations, as well as coordinating between vehicles so that they do not attempt to pick up the same passenger. While prior works have developed models for this uncertainty and used them to optimize dispatch policies, in this work we introduce a model-free approach. Specifically, we propose MOVI, a Deep Q-network (DQN)-based framework that directly learns the optimal vehicle dispatch policy. Since DQNs scale poorly with a large number of possible dispatches, we streamline our DQN training and suppose that each individual vehicle independently learns its own optimal policy, ensuring scalability at the cost of less coordination between vehicles. We then formulate a centralized receding-horizon control (RHC) policy to compare with our DQN policies. To compare these policies, we design and build MOVI as a large-scale realistic simulator based on 15 million taxi trip records that simulates policy-agnostic responses to dispatch decisions. We show that the DQN dispatch policy reduces the number of unserviced requests by 76% compared to without dispatch and 20% compared to the RHC approach, emphasizing the benefits of a model-free approach and suggesting that there is limited value to coordinating vehicle actions. This finding may help to explain the success of ridesharing platforms, for which drivers make individual decisions. |
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| Published | 2018-04-13 |
| URL | http://arxiv.org/abs/1804.04758v1 |
| http://arxiv.org/pdf/1804.04758v1.pdf | |
| PWC | https://paperswithcode.com/paper/movi-a-model-free-approach-to-dynamic-fleet |
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CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal
| Title | CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal |
| Authors | Kunal Swami, Saikat Kumar Das |
| Abstract | Single image haze removal is a very challenging and ill-posed problem. The existing haze removal methods in literature, including the recently introduced deep learning methods, model the problem of haze removal as that of estimating intermediate parameters, viz., scene transmission map and atmospheric light. These are used to compute the haze-free image from the hazy input image. Such an approach only focuses on accurate estimation of intermediate parameters, while the aesthetic quality of the haze-free image is unaccounted for in the optimization framework. Thus, errors in the estimation of intermediate parameters often lead to generation of inferior quality haze-free images. In this paper, we present CANDY (Conditional Adversarial Networks based Dehazing of hazY images), a fully end-to-end model which directly generates a clean haze-free image from a hazy input image. CANDY also incorporates the visual quality of haze-free image into the optimization function; thus, generating a superior quality haze-free image. To the best of our knowledge, this is the first work in literature to propose a fully end-to-end model for single image haze removal. Also, this is the first work to explore the newly introduced concept of generative adversarial networks for the problem of single image haze removal. The proposed model CANDY was trained on a synthetically created haze image dataset, while evaluation was performed on challenging synthetic as well as real haze image datasets. The extensive evaluation and comparison results of CANDY reveal that it significantly outperforms existing state-of-the-art haze removal methods in literature, both quantitatively as well as qualitatively. |
| Tasks | Single Image Haze Removal |
| Published | 2018-01-09 |
| URL | http://arxiv.org/abs/1801.02892v2 |
| http://arxiv.org/pdf/1801.02892v2.pdf | |
| PWC | https://paperswithcode.com/paper/candy-conditional-adversarial-networks-based |
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The problem of the development ontology-driven architecture of intellectual software systems
| Title | The problem of the development ontology-driven architecture of intellectual software systems |
| Authors | A. V. Palagin, N. G. Petrenko, V. Yu. Velychko, K. S. Malakhov |
| Abstract | The paper describes the architecture of the intelligence system for automated design of ontological knowledge bases of domain areas and the software model of the management GUI (Graphical User Interface) subsystem |
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| Published | 2018-02-17 |
| URL | http://arxiv.org/abs/1802.06767v2 |
| http://arxiv.org/pdf/1802.06767v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-problem-of-the-development-ontology |
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Training Neural Networks Using Features Replay
| Title | Training Neural Networks Using Features Replay |
| Authors | Zhouyuan Huo, Bin Gu, Heng Huang |
| Abstract | Training a neural network using backpropagation algorithm requires passing error gradients sequentially through the network. The backward locking prevents us from updating network layers in parallel and fully leveraging the computing resources. Recently, there are several works trying to decouple and parallelize the backpropagation algorithm. However, all of them suffer from severe accuracy loss or memory explosion when the neural network is deep. To address these challenging issues, we propose a novel parallel-objective formulation for the objective function of the neural network. After that, we introduce features replay algorithm and prove that it is guaranteed to converge to critical points for the non-convex problem under certain conditions. Finally, we apply our method to training deep convolutional neural networks, and the experimental results show that the proposed method achieves {faster} convergence, {lower} memory consumption, and {better} generalization error than compared methods. |
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| Published | 2018-07-12 |
| URL | https://arxiv.org/abs/1807.04511v5 |
| https://arxiv.org/pdf/1807.04511v5.pdf | |
| PWC | https://paperswithcode.com/paper/training-neural-networks-using-features |
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Analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem
| Title | Analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem |
| Authors | Patrick Spettel, Hans-Georg Beyer |
| Abstract | A theoretical performance analysis of the $(\mu/\mu_I,\lambda)$-$\sigma$-Self-Adaptation Evolution Strategy ($\sigma$SA-ES) is presented considering a conically constrained problem. Infeasible offspring are repaired using projection onto the boundary of the feasibility region. Closed-form approximations are used for the one-generation progress of the evolution strategy. Approximate deterministic evolution equations are formulated for analyzing the strategy’s dynamics. By iterating the evolution equations with the approximate one-generation expressions, the evolution strategy’s dynamics can be predicted. The derived theoretical results are compared to experiments for assessing the approximation quality. It is shown that in the steady state the $(\mu/\mu_I,\lambda)$-$\sigma$SA-ES exhibits a performance as if the ES were optimizing a sphere model. Unlike the non-recombinative $(1,\lambda)$-ES, the parental steady state behavior does not evolve on the cone boundary but stays away from the boundary to a certain extent. |
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| Published | 2018-12-15 |
| URL | http://arxiv.org/abs/1812.06300v1 |
| http://arxiv.org/pdf/1812.06300v1.pdf | |
| PWC | https://paperswithcode.com/paper/analysis-of-the-_i-self-adaptation-evolution |
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