Paper Group ANR 267
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting. Catching Loosely Synchronized Behavior in Face of Camouflage. Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks. A Compact Network Learning Model for Distribution Regression. CoSpace: Common Subspace Lear …
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting
| Title | A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting |
| Authors | Samarth Gupta, Shreyas Chaudhari, Subhojyoti Mukherjee, Gauri Joshi, Osman Yağan |
| Abstract | We consider a finite-armed structured bandit problem in which mean rewards of different arms are functions of a common hidden parameter $\theta^$. This problem setting subsumes several previously studied frameworks that assume linear or invertible reward functions. Our approach exploits the structure in the problem gradually and pulls only some of the sub-optimal arms O(log T) times, while other sub-optimal arms, termed as non-competitive, are pulled only O(1) times. Put differently, the set of non-competitive arms, which depend on the hidden parameter $\theta^$, are stopped being pulled after some finite time. We show how this approach can be transformed into a general algorithm that can be coupled with any classic bandit strategy (UCB, Thompson Sampling, KL-UCB etc.), allowing them to be used in the structured bandit setting with substantial reductions in regret. In particular, we get bounded regret in several cases of practical interest where all sub-optimal arms are non-competitive. We also demonstrate the superiority of our algorithms over existing methods (including UCB-S) via experiments on the Movielens dataset. |
| Tasks | |
| Published | 2018-10-18 |
| URL | https://arxiv.org/abs/1810.08164v5 |
| https://arxiv.org/pdf/1810.08164v5.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-correlation-in-finite-armed |
| Repo | |
| Framework | |
Catching Loosely Synchronized Behavior in Face of Camouflage
| Title | Catching Loosely Synchronized Behavior in Face of Camouflage |
| Authors | Yikun Ban, Jiao Sun, Xin Liu |
| Abstract | Fraud has severely detrimental impacts on the business of social networks and other online applications. A user can become a fake celebrity by purchasing “zombie followers” on Twitter. A merchant can boost his reputation through fake reviews on Amazon. This phenomenon also conspicuously exists on Facebook, Yelp and TripAdvisor, etc. In all the cases, fraudsters try to manipulate the platform’s ranking mechanism by faking interactions between the fake accounts they control and the target customers. |
| Tasks | |
| Published | 2018-10-21 |
| URL | http://arxiv.org/abs/1810.08885v1 |
| http://arxiv.org/pdf/1810.08885v1.pdf | |
| PWC | https://paperswithcode.com/paper/catching-loosely-synchronized-behavior-in |
| Repo | |
| Framework | |
Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
| Title | Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks |
| Authors | Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan |
| Abstract | Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep Neural Networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a 5-layer phase-only D2NN jointly-optimized with a single fully-connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10x10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design. |
| Tasks | |
| Published | 2018-10-03 |
| URL | https://arxiv.org/abs/1810.01916v4 |
| https://arxiv.org/pdf/1810.01916v4.pdf | |
| PWC | https://paperswithcode.com/paper/analysis-of-diffractive-optical-neural |
| Repo | |
| Framework | |
A Compact Network Learning Model for Distribution Regression
| Title | A Compact Network Learning Model for Distribution Regression |
| Authors | Connie Kou, Hwee Kuan Lee, Teck Khim Ng |
| Abstract | Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Our proposed Distribution Regression Network (DRN) achieves higher prediction accuracies while being much more compact and uses fewer parameters than traditional neural networks. |
| Tasks | |
| Published | 2018-04-13 |
| URL | http://arxiv.org/abs/1804.04775v3 |
| http://arxiv.org/pdf/1804.04775v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-compact-network-learning-model-for |
| Repo | |
| Framework | |
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
| Title | CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences |
| Authors | Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu |
| Abstract | With a large amount of open satellite multispectral imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global multispectral land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-multispectral(HS-MS) correspondences. The multispectral out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HSMS datasets (University of Houston and Chikusei), where HS-MS data sets have trade-offs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods. |
| Tasks | |
| Published | 2018-12-30 |
| URL | http://arxiv.org/abs/1812.11501v2 |
| http://arxiv.org/pdf/1812.11501v2.pdf | |
| PWC | https://paperswithcode.com/paper/cospace-common-subspace-learning-from |
| Repo | |
| Framework | |
MemeSequencer: Sparse Matching for Embedding Image Macros
| Title | MemeSequencer: Sparse Matching for Embedding Image Macros |
| Authors | Abhimanyu Dubey, Esteban Moro, Manuel Cebrian, Iyad Rahwan |
| Abstract | The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or `memes’. However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each. In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery. | |
| Tasks | Image Clustering, Image Retrieval |
| Published | 2018-02-14 |
| URL | http://arxiv.org/abs/1802.04936v1 |
| http://arxiv.org/pdf/1802.04936v1.pdf | |
| PWC | https://paperswithcode.com/paper/memesequencer-sparse-matching-for-embedding |
| Repo | |
| Framework | |
Word Embeddings from Large-Scale Greek Web Content
| Title | Word Embeddings from Large-Scale Greek Web Content |
| Authors | Stamatis Outsios, Konstantinos Skianis, Polykarpos Meladianos, Christos Xypolopoulos, Michalis Vazirgiannis |
| Abstract | Word embeddings are undoubtedly very useful components in many NLP tasks. In this paper, we present word embeddings and other linguistic resources trained on the largest to date digital Greek language corpus. We also present a live web tool for testing the Greek word embeddings, by offering “analogy”, “similarity score” and “most similar words” functions. Through our explorer, one could interact with the Greek word vectors. |
| Tasks | Word Embeddings |
| Published | 2018-10-08 |
| URL | http://arxiv.org/abs/1810.06694v3 |
| http://arxiv.org/pdf/1810.06694v3.pdf | |
| PWC | https://paperswithcode.com/paper/word-embeddings-from-large-scale-greek-web |
| Repo | |
| Framework | |
Uncovering the Spread of Chagas Disease in Argentina and Mexico
| Title | Uncovering the Spread of Chagas Disease in Argentina and Mexico |
| Authors | Juan de Monasterio, Alejo Salles, Carolina Lang, Diego Weinberg, Martin Minnoni, Matias Travizano, Carlos Sarraute |
| Abstract | Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobility information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies in Latin American countries. Our objective is to generate risk maps which can be used by public health campaign managers to prioritize detection campaigns and target specific areas. Finally, we analyze the value of mobile phone data to infer long-term migrations, which play a crucial role in the geographical spread of Chagas disease. |
| Tasks | |
| Published | 2018-08-09 |
| URL | http://arxiv.org/abs/1808.03350v1 |
| http://arxiv.org/pdf/1808.03350v1.pdf | |
| PWC | https://paperswithcode.com/paper/uncovering-the-spread-of-chagas-disease-in |
| Repo | |
| Framework | |
Optimization-Based Algorithm for Evolutionarily Stable Strategies against Pure Mutations
| Title | Optimization-Based Algorithm for Evolutionarily Stable Strategies against Pure Mutations |
| Authors | Sam Ganzfried |
| Abstract | Evolutionarily stable strategy (ESS) is an important solution concept in game theory which has been applied frequently to biological models. Informally an ESS is a strategy that if followed by the population cannot be taken over by a mutation strategy that is initially rare. Finding such a strategy has been shown to be difficult from a theoretical complexity perspective. We present an algorithm for the case where mutations are restricted to pure strategies, and present experiments on several game classes including random and a recently-proposed cancer model. Our algorithm is based on a mixed-integer non-convex feasibility program formulation, which constitutes the first general optimization formulation for this problem. It turns out that the vast majority of the games included in the experiments contain ESS with small support, and our algorithm is outperformed by a support-enumeration based approach. However we suspect our algorithm may be useful in the future as games are studied that have ESS with potentially larger and unknown support size. |
| Tasks | |
| Published | 2018-03-01 |
| URL | http://arxiv.org/abs/1803.00607v2 |
| http://arxiv.org/pdf/1803.00607v2.pdf | |
| PWC | https://paperswithcode.com/paper/optimization-based-algorithm-for |
| Repo | |
| Framework | |
Poisoning Attacks to Graph-Based Recommender Systems
| Title | Poisoning Attacks to Graph-Based Recommender Systems |
| Authors | Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, Jia Liu |
| Abstract | Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacker desires. However, these poisoning attacks are either agnostic to recommendation algorithms or optimized to recommender systems that are not graph-based. Like association-rule-based and matrix-factorization-based recommender systems, graph-based recommender system is also deployed in practice, e.g., eBay, Huawei App Store. However, how to design optimized poisoning attacks for graph-based recommender systems is still an open problem. In this work, we perform a systematic study on poisoning attacks to graph-based recommender systems. Due to limited resources and to avoid detection, we assume the number of fake users that can be injected into the system is bounded. The key challenge is how to assign rating scores to the fake users such that the target item is recommended to as many normal users as possible. To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users. We also propose techniques to solve the optimization problem. We evaluate our attacks and compare them with existing attacks under white-box (recommendation algorithm and its parameters are known), gray-box (recommendation algorithm is known but its parameters are unknown), and black-box (recommendation algorithm is unknown) settings using two real-world datasets. Our results show that our attack is effective and outperforms existing attacks for graph-based recommender systems. For instance, when 1% fake users are injected, our attack can make a target item recommended to 580 times more normal users in certain scenarios. |
| Tasks | Recommendation Systems |
| Published | 2018-09-11 |
| URL | http://arxiv.org/abs/1809.04127v1 |
| http://arxiv.org/pdf/1809.04127v1.pdf | |
| PWC | https://paperswithcode.com/paper/poisoning-attacks-to-graph-based-recommender |
| Repo | |
| Framework | |
Dynamic Feature Acquisition Using Denoising Autoencoders
| Title | Dynamic Feature Acquisition Using Denoising Autoencoders |
| Authors | Mohammad Kachuee, Sajad Darabi, Babak Moatamed, Majid Sarrafzadeh |
| Abstract | In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers. In the proposed architecture, a denoising autoencoder is used to handle unknown features (i.e., features that are yet to be acquired), and the sensitivity of predictions with respect to each unknown feature is used as a context-dependent measure of informativeness. We evaluated the proposed method on eight different real-world datasets as well as one synthesized dataset and compared its performance with several other approaches in the literature. According to the results, the suggested method is capable of efficiently acquiring features at test-time in a cost- and context-aware fashion. |
| Tasks | Denoising, Density Estimation |
| Published | 2018-11-03 |
| URL | http://arxiv.org/abs/1811.01249v1 |
| http://arxiv.org/pdf/1811.01249v1.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-feature-acquisition-using-denoising |
| Repo | |
| Framework | |
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
| Title | Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification |
| Authors | Kai-Xuan Chen, Xiao-Jun Wu, Rui Wang, Josef Kittler |
| Abstract | In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been widely adopted for the task of image set classification. Recently, it has been proven that infinite-dimensional CovDs are more discriminative than their low-dimensional counterparts. However, the form of infinite-dimensional CovDs is implicit and the computational load is high. We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr"om method based on a Riemannian kernel. We start by modeling the images via CovDs, which lie on the Riemannian manifold spanned by SPD (Symmetric Positive Definite) matrices. We then extend the Nystr"om method to the SPD manifold and obtain the approximations of CovDs in RKHS (Reproducing Kernel Hilbert Space). Finally, we approximate infinite-dimensional CovDs via these approximations. Empirically, we apply our framework to the task of image set classification. The experimental results obtained on three benchmark datasets show that our proposed approximate infinite-dimensional CovDs outperform the original CovDs. |
| Tasks | |
| Published | 2018-06-16 |
| URL | https://arxiv.org/abs/1806.06177v2 |
| https://arxiv.org/pdf/1806.06177v2.pdf | |
| PWC | https://paperswithcode.com/paper/riemannian-kernel-based-nystrom-method-for |
| Repo | |
| Framework | |
Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
| Title | Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification |
| Authors | Xiaohui Yang, Xiaoying Jiang, Wenming Wu, Juan Zhang, Dan Long, Funa Zhou, Yiming Xu |
| Abstract | Sparse representation classification achieves good results by addressing recognition problem with sufficient training samples per subject. However, SRC performs not very well for small sample data. In this paper, an inverse-projection group sparse representation model is presented for breast tumor classification, which is based on constructing low-rank variation dictionary. The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples. The inverse projection group sparsity representation model is constructed based on taking full using of exist samples and group effect of microarray gene data. Extensive experiments on public breast tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3 databases are 80.81%, 89.10% and 100% respectively, which are better than the latest literature. |
| Tasks | |
| Published | 2018-03-10 |
| URL | http://arxiv.org/abs/1803.04793v1 |
| http://arxiv.org/pdf/1803.04793v1.pdf | |
| PWC | https://paperswithcode.com/paper/low-rank-variation-dictionary-and-inverse |
| Repo | |
| Framework | |
Cross-lingual Candidate Search for Biomedical Concept Normalization
| Title | Cross-lingual Candidate Search for Biomedical Concept Normalization |
| Authors | Roland Roller, Madeleine Kittner, Dirk Weissenborn, Ulf Leser |
| Abstract | Biomedical concept normalization links concept mentions in texts to a semantically equivalent concept in a biomedical knowledge base. This task is challenging as concepts can have different expressions in natural languages, e.g. paraphrases, which are not necessarily all present in the knowledge base. Concept normalization of non-English biomedical text is even more challenging as non-English resources tend to be much smaller and contain less synonyms. To overcome the limitations of non-English terminologies we propose a cross-lingual candidate search for concept normalization using a character-based neural translation model trained on a multilingual biomedical terminology. Our model is trained with Spanish, French, Dutch and German versions of UMLS. The evaluation of our model is carried out on the French Quaero corpus, showing that it outperforms most teams of CLEF eHealth 2015 and 2016. Additionally, we compare performance to commercial translators on Spanish, French, Dutch and German versions of Mantra. Our model performs similarly well, but is free of charge and can be run locally. This is particularly important for clinical NLP applications as medical documents underlay strict privacy restrictions. |
| Tasks | |
| Published | 2018-05-04 |
| URL | http://arxiv.org/abs/1805.01646v1 |
| http://arxiv.org/pdf/1805.01646v1.pdf | |
| PWC | https://paperswithcode.com/paper/cross-lingual-candidate-search-for-biomedical |
| Repo | |
| Framework | |
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
| Title | A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning |
| Authors | Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan |
| Abstract | Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$, unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when $\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar to other multi-step temporal-difference learning algorithms, $Q(\sigma)$ needs much memory consumption and computation time. Eligibility trace is an important mechanism to transform the off-line updates into efficient on-line ones which consume less memory and computation time. In this paper, we further develop the original $Q(\sigma)$, combine it with eligibility traces and propose a new algorithm, called $Q(\sigma ,\lambda)$, in which $\lambda$ is trace-decay parameter. This idea unifies Sarsa$(\lambda)$ (when $\sigma =1$) and $Q^{\pi}(\lambda)$ (when $\sigma =0$). Furthermore, we give an upper error bound of $Q(\sigma ,\lambda)$ policy evaluation algorithm. We prove that $Q(\sigma,\lambda)$ control algorithm can converge to the optimal value function exponentially. We also empirically compare it with conventional temporal-difference learning methods. Results show that, with an intermediate value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end ($\sigma=0$, or $1$). |
| Tasks | |
| Published | 2018-02-09 |
| URL | http://arxiv.org/abs/1802.03171v1 |
| http://arxiv.org/pdf/1802.03171v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-unified-approach-for-multi-step-temporal |
| Repo | |
| Framework | |
