Paper Group ANR 43
A Dimension-free Algorithm for Contextual Continuum-armed Bandits. Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier. Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems. Using Micro-collections in Social Media to Generate Seeds for Web Archive Collections. Nonparametric …
A Dimension-free Algorithm for Contextual Continuum-armed Bandits
| Title | A Dimension-free Algorithm for Contextual Continuum-armed Bandits |
| Authors | Wenhao Li, Ningyuan Chen, L. Jeff Hong |
| Abstract | In contextual continuum-armed bandits, the contexts $x$ and the arms $y$ are both continuous and drawn from high-dimensional spaces. The payoff function to learn $f(x,y)$ does not have a particular parametric form. The literature has shown that for Lipschitz-continuous functions, the optimal regret is $\tilde{O}(T^{\frac{d_x+d_y+1}{d_x+d_y+2}})$, where $d_x$ and $d_y$ are the dimensions of contexts and arms, and thus suffers from the curse of dimensionality. We develop an algorithm that achieves regret $\tilde{O}(T^{\frac{d_x+1}{d_x+2}})$ when $f$ is globally concave in $y$. The global concavity is a common assumption in many applications. The algorithm is based on stochastic approximation and estimates the gradient information in an online fashion. Our results generate a valuable insight that the curse of dimensionality of the arms can be overcome with some mild structures of the payoff function. |
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| Published | 2019-07-15 |
| URL | https://arxiv.org/abs/1907.06550v2 |
| https://arxiv.org/pdf/1907.06550v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-dimension-free-algorithm-for-contextual |
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Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier
| Title | Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier |
| Authors | Yuyang Zhou, Guang Cheng, Shanqing Jiang, Mian Dai |
| Abstract | Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. |
| Tasks | Anomaly Detection, Dimensionality Reduction, Feature Selection, Intrusion Detection, Network Intrusion Detection |
| Published | 2019-04-02 |
| URL | https://arxiv.org/abs/1904.01352v3 |
| https://arxiv.org/pdf/1904.01352v3.pdf | |
| PWC | https://paperswithcode.com/paper/an-efficient-network-intrusion-detection |
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Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems
| Title | Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems |
| Authors | Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer |
| Abstract | In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence of spatially localized patterns. However, findings of self-organized patterns in such models have so far relied on manual tuning of parameters and initial states, and on the human eye to identify interesting patterns. In this paper, we formulate the problem of automated discovery of diverse self-organized patterns in such high-dimensional complex dynamical systems, as well as a framework for experimentation and evaluation. Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area. These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations. Goal space representations describe the interesting features of patterns for which diverse variations should be discovered. In particular, we compare various approaches to define and learn goal space representations from the perspective of discovering diverse spatially localized patterns. Moreover, we introduce an extension of a state-of-the-art POP-IMGEP algorithm which incrementally learns a goal representation using a deep auto-encoder, and the use of CPPN primitives for generating initialization parameters. We show that it is more efficient than several baselines and equally efficient as a system pre-trained on a hand-made database of patterns identified by human experts. |
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| Published | 2019-08-19 |
| URL | https://arxiv.org/abs/1908.06663v3 |
| https://arxiv.org/pdf/1908.06663v3.pdf | |
| PWC | https://paperswithcode.com/paper/intrinsically-motivated-exploration-for |
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Using Micro-collections in Social Media to Generate Seeds for Web Archive Collections
| Title | Using Micro-collections in Social Media to Generate Seeds for Web Archive Collections |
| Authors | Alexander C. Nwala, Michele C. Weigle, Michael L. Nelson |
| Abstract | In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events ranging from elections to disease outbreaks. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but it is time consuming to collect these seeds. Two main strategies adopted by curators for discovering seeds include scraping Web (e.g., Google) Search Engine Result Pages (SERPs) and social media (e.g., Twitter) SERPs. In this work, we studied three social media platforms in order to provide insight on the characteristics of seeds generated from different sources. First, we developed a simple vocabulary for describing social media posts across different platforms. Second, we introduced a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share posts about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts micro-collections, and we consider them as an important source for seeds because the effort taken to create micro-collections is an indication of editorial activity, and a demonstration of domain expertise. Third, we generated 23,112 seed collections with text and hashtag queries from 449,347 social media posts from Reddit, Twitter, and Scoop.it. We collected in total 120,444 URIs from the conventional scraped SERP posts and micro-collections. We characterized the resultant seed collections across multiple dimensions including the distribution of URIs, precision, ages, diversity of webpages, etc… |
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| Published | 2019-05-29 |
| URL | https://arxiv.org/abs/1905.12220v1 |
| https://arxiv.org/pdf/1905.12220v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-micro-collections-in-social-media-to |
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Nonparametric learning for impulse control problems
| Title | Nonparametric learning for impulse control problems |
| Authors | Sören Christensen, Claudia Strauch |
| Abstract | One of the fundamental assumptions in stochastic control of continuous time processes is that the dynamics of the underlying (diffusion) process is known. This is, however, usually obviously not fulfilled in practice. On the other hand, over the last decades, a rich theory for nonparametric estimation of the drift (and volatility) for continuous time processes has been developed. The aim of this paper is bringing together techniques from stochastic control with methods from statistics for stochastic processes to find a way to both learn the dynamics of the underlying process and control in a reasonable way at the same time. More precisely, we study a long-term average impulse control problem, a stochastic version of the classical Faustmann timber harvesting problem. One of the problems that immediately arises is an exploration vs. exploitation-behavior as is well known for problems in machine learning. We propose a way to deal with this issue by combining exploration- and exploitation periods in a suitable way. Our main finding is that this construction can be based on the rates of convergence of estimators for the invariant density. Using this, we obtain that the average cumulated regret is of uniform order $O({T^{-1/3}})$. |
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| Published | 2019-09-20 |
| URL | https://arxiv.org/abs/1909.09528v1 |
| https://arxiv.org/pdf/1909.09528v1.pdf | |
| PWC | https://paperswithcode.com/paper/nonparametric-learning-for-impulse-control |
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Multilingual Neural Machine Translation with Language Clustering
| Title | Multilingual Neural Machine Translation with Language Clustering |
| Authors | Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu |
| Abstract | Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods |
| Tasks | Machine Translation |
| Published | 2019-08-25 |
| URL | https://arxiv.org/abs/1908.09324v1 |
| https://arxiv.org/pdf/1908.09324v1.pdf | |
| PWC | https://paperswithcode.com/paper/multilingual-neural-machine-translation-with-6 |
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Self-Adjusting Mutation Rates with Provably Optimal Success Rules
| Title | Self-Adjusting Mutation Rates with Provably Optimal Success Rules |
| Authors | Benjamin Doerr, Carola Doerr, Johannes Lengler |
| Abstract | The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms. While it is often applied in the literal sense, a common interpretation sees the one-fifth success rule as a family of success-based updated rules that are determined by an update strength $F$ and a success rate $s$. We analyze in this work how the performance of the (1+1) Evolutionary Algorithm (EA) on LeadingOnes depends on these two hyper-parameters. Our main result shows that the best performance is obtained for small update strengths $F=1+o(1)$ and success rate $1/e$. We also prove that the running time obtained by this parameter setting is asymptotically optimal among all dynamic choices of the mutation rate for the (1+1) EA, up to lower order error terms. We show similar results for the resampling variant of the (1+1) EA, which enforces to flip at least one bit per iteration. |
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| Published | 2019-02-07 |
| URL | https://arxiv.org/abs/1902.02588v2 |
| https://arxiv.org/pdf/1902.02588v2.pdf | |
| PWC | https://paperswithcode.com/paper/self-adjusting-mutation-rates-with-provably |
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Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
| Title | Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors |
| Authors | Antoine Cully |
| Abstract | Quality-Diversity optimization is a new family of optimization algorithms that, instead of searching for a single optimal solution to solving a task, searches for a large collection of solutions that all solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as such a diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whether two solutions are different or similar. The choice of a behavioral descriptor is crucial, as it completely changes the solution types that the algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversity algorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the range of their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot can autonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to handcrafted solutions that uses domain knowledge and significantly more diverse than when using existing unsupervised methods. |
| Tasks | Dimensionality Reduction |
| Published | 2019-05-28 |
| URL | https://arxiv.org/abs/1905.11874v1 |
| https://arxiv.org/pdf/1905.11874v1.pdf | |
| PWC | https://paperswithcode.com/paper/autonomous-skill-discovery-with-quality |
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Defining and Unpacking Transformative AI
| Title | Defining and Unpacking Transformative AI |
| Authors | Ross Gruetzemacher, Jess Whittlestone |
| Abstract | Recently the concept of transformative AI (TAI) has begun to receive attention in the AI policy space. TAI is often framed as an alternative formulation to notions of strong AI (e.g. artificial general intelligence or superintelligence) and reflects increasing consensus that advanced AI which does not fit these definitions may nonetheless have extreme and long-lasting impacts on society. However, the term TAI is poorly defined and often used ambiguously. Some use the notion of TAI to describe levels of societal transformation associated with previous ‘general purpose technologies’ (GPTs) such as electricity or the internal combustion engine. Others use the term to refer to more drastic levels of transformation comparable to the agricultural or industrial revolutions. The notion has also been used much more loosely, with some implying that current AI systems are already having a transformative impact on society. This paper unpacks and analyses the notion of TAI, proposing a distinction between TAI and radically transformative AI (RTAI), roughly corresponding to societal change on the level of the agricultural or industrial revolutions. We describe some relevant dimensions associated with each and discuss what kinds of advances in capabilities they might require. We further consider the relationship between TAI and RTAI and whether we should necessarily expect a period of TAI to precede the emergence of RTAI. This analysis is important as it can help guide discussions among AI policy researchers about how to allocate resources towards mitigating the most extreme impacts of AI and it can bring attention to negative TAI scenarios that are currently neglected. |
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| Published | 2019-11-27 |
| URL | https://arxiv.org/abs/1912.00747v1 |
| https://arxiv.org/pdf/1912.00747v1.pdf | |
| PWC | https://paperswithcode.com/paper/defining-and-unpacking-transformative-ai |
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A Reduction from Reinforcement Learning to No-Regret Online Learning
| Title | A Reduction from Reinforcement Learning to No-Regret Online Learning |
| Authors | Ching-An Cheng, Remi Tachet des Combes, Byron Boots, Geoff Gordon |
| Abstract | We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which “any” online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard online-learning analysis, and the second part can be quantified independently of the learning algorithm. Therefore, the proposed reduction can be used as a tool to systematically design new RL algorithms. We demonstrate this idea by devising a simple RL algorithm based on mirror descent and the generative-model oracle. For any $\gamma$-discounted tabular RL problem, with probability at least $1-\delta$, it learns an $\epsilon$-optimal policy using at most $\tilde{O}\left(\frac{\mathcal{S}\mathcal{A}\log(\frac{1}{\delta})}{(1-\gamma)^4\epsilon^2}\right)$ samples. Furthermore, this algorithm admits a direct extension to linearly parameterized function approximators for large-scale applications, with computation and sample complexities independent of $\mathcal{S}$,$\mathcal{A}$, though at the cost of potential approximation bias. |
| Tasks | |
| Published | 2019-11-14 |
| URL | https://arxiv.org/abs/1911.05873v2 |
| https://arxiv.org/pdf/1911.05873v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-reduction-from-reinforcement-learning-to-no |
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Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing
| Title | Improving Referring Expression Grounding with Cross-modal Attention-guided Erasing |
| Authors | Xihui Liu, Zihao Wang, Jing Shao, Xiaogang Wang, Hongsheng Li |
| Abstract | Referring expression grounding aims at locating certain objects or persons in an image with a referring expression, where the key challenge is to comprehend and align various types of information from visual and textual domain, such as visual attributes, location and interactions with surrounding regions. Although the attention mechanism has been successfully applied for cross-modal alignments, previous attention models focus on only the most dominant features of both modalities, and neglect the fact that there could be multiple comprehensive textual-visual correspondences between images and referring expressions. To tackle this issue, we design a novel cross-modal attention-guided erasing approach, where we discard the most dominant information from either textual or visual domains to generate difficult training samples online, and to drive the model to discover complementary textual-visual correspondences. Extensive experiments demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance on three referring expression grounding datasets. |
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| Published | 2019-03-03 |
| URL | http://arxiv.org/abs/1903.00839v2 |
| http://arxiv.org/pdf/1903.00839v2.pdf | |
| PWC | https://paperswithcode.com/paper/improving-referring-expression-grounding-with |
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Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
| Title | Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization |
| Authors | Weisen Wang, Zhiyan Xu, Weihong Yu, Jianchun Zhao, Jingyuan Yang, Feng He, Zhikun Yang, Di Chen, Dayong Ding, Youxin Chen, Xirong Li |
| Abstract | This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN’s fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we introduce Loose Pair training, where a fundus image and an OCT image are paired based on class labels rather than eyes. Moreover, for a visual interpretation of how the individual modalities make contributions, we extend the class activation mapping technique to the multi-modal scenario. Experiments on a real-world dataset collected from an outpatient clinic justify the viability of our proposal for multi-modal AMD categorization. |
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| Published | 2019-07-28 |
| URL | https://arxiv.org/abs/1907.12023v1 |
| https://arxiv.org/pdf/1907.12023v1.pdf | |
| PWC | https://paperswithcode.com/paper/two-stream-cnn-with-loose-pair-training-for |
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New method for shape recognition based on dynamic programming
| Title | New method for shape recognition based on dynamic programming |
| Authors | Noreddine Gherabi, Mohamed Bahaj |
| Abstract | In this paper we present a new method for shape recognition based on dynamic programming. First, each contour of shape is represented by a set of points. After alignment and matching between two shapes, the outline of the shape is divided into parts according to N angular and M radial sectors , Each Sector contains a portion of the contour; this portion is divided at the inflexion points into convex and concave sections, and the information about sections are extracted in order to provide a semantic content to the outline shape, then this information are coded and transformed into a string of symbols. Finally we find the best alignment of two complete strings and compute the optimal cost of similarity. The algorithm has been tested on a large set of shape databases and real images (MPEG-7, natural silhouette database). |
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| Published | 2019-04-04 |
| URL | http://arxiv.org/abs/1904.08501v1 |
| http://arxiv.org/pdf/1904.08501v1.pdf | |
| PWC | https://paperswithcode.com/paper/190408501 |
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Amur Tiger Re-identification in the Wild
| Title | Amur Tiger Re-identification in the Wild |
| Authors | Shuyuan Li, Jianguo Li, Weiyao Lin, Hanlin Tang |
| Abstract | Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while applying computer vision methods to camera sensor data requires re-identification (re-ID) algorithms to obtain accurate counts and moving trajectory of wildlife. However, existing re-ID methods are largely targeted at persons and cars, which have limited pose variations and constrained capture environments. This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. In contrast to typical re-ID datasets, the tigers are captured in a diverse set of unconstrained poses and lighting conditions. We demonstrate with a set of baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we propose a novel method for tiger re-identification, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods. The dataset will be public available at https://cvwc2019.github.io/ . |
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| Published | 2019-06-13 |
| URL | https://arxiv.org/abs/1906.05586v2 |
| https://arxiv.org/pdf/1906.05586v2.pdf | |
| PWC | https://paperswithcode.com/paper/amur-tiger-re-identification-in-the-wild |
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Indoor Information Retrieval using Lifelog Data
| Title | Indoor Information Retrieval using Lifelog Data |
| Authors | Deepanwita Datta |
| Abstract | Studying human behaviour through lifelogging has seen an increase in attention from researchers over the past decade. The opportunities that lifelogging offers are based on the fact that a lifelog, as a “black box” of our lives, offers rich contextual information, which has been an Achilles heel of information discovery. While lifelog data has been put to use in various contexts, its application to indoor environment scenario remains unexplored. In this proposal, I plan to design a method that enables us to capture and record indoor lifelog data of a person’s life in order to facilitate healthcare systems, emergency response, item tracking etc. To this end, we aim to build an Indoor Information Retrieval system that can be queried with natural language queries over lifelog data. Judicious use of the lifelog data for the indoor application may enable us to solve very fundamental but non-avoidable problems of our daily life. Analysis of lifelog data coupled with Information Retrieval is not only a promising research topic, but the possibility of its indoor application especially for healthcare, lost-item tracking would be an innovative research idea to the best of our knowledge. |
| Tasks | Information Retrieval |
| Published | 2019-10-17 |
| URL | https://arxiv.org/abs/1910.07784v1 |
| https://arxiv.org/pdf/1910.07784v1.pdf | |
| PWC | https://paperswithcode.com/paper/indoor-information-retrieval-using-lifelog |
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