aid stringlengths 9 15 | mid stringlengths 7 10 | abstract stringlengths 78 2.56k | related_work stringlengths 92 1.77k | ref_abstract dict |
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1907.08302 | 2964151964 | With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution ... | The mentioned data sender emits data to the DSPS, which is mostly car position reports. Depending on the overall situation on the expressways, car position reports may require the DSPS to create an output or not. Next to car position reports, the remaining input data represent an explicit query which always requires an... | {
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"This paper specifies the Linear Road Benchmark for Stream Data Management Systems (SDMS). Stream Data Management Systems process streaming data by executing continuous and historical queries while producing query results in real-t... |
1907.08302 | 2964151964 | With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution ... | The benchmark result for a system is summarized as a so called L-rating. This metric defined by Linear Road expresses how many expressways the system could handle while meeting the defined response time requirements for each query. A higher number of expressways corresponds to a higher data input rate for the SUT. When... | {
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"Abstract.This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data pro... |
1907.08302 | 2964151964 | With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution ... | The work presented in @cite_11 compares Apache Flink and Apache Spark. The conducted measurements include different queries, a grep query being one of them. One focus area that is analyzed is the scaling behavior with regard to different numbers of nodes in the cluster. However, studying both systems from a data stream... | {
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"Big Data analytics has recently gained increasing popularity as a tool to process large amounts of data on-demand. Spark and Flink are two Apache-hosted data analytics frameworks that facilitate the development of multi-step data ... |
1907.08302 | 2964151964 | With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution ... | @cite_20 compare Apache Storm, Apache Flink, and Apache Spark Streaming in their paper. Besides describing the architecture of these three systems, the performance is studied in a network traffic analysis scenario. Additionally, the behavior in case of a node failure is investigated. | {
"cite_N": [
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"Distributed stream processing platforms is a new class of real-time monitoring systems that analyze and extracts knowledge from large continuous streams of data. This type of systems is crucial for providing high throughput and lo... |
1902.00126 | 2915013032 | We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections.... | The most prominent work for stochastic optimization problems is stochastic gradient descent (SGD) @cite_17 @cite_13 @cite_5 . Even though SGD is very well studied, it only applies when there does not exist any constraints in the problem template . For the case of simple constraints, @math @math in and almost sure const... | {
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"A new recursive algorithm of stochastic approximation type with the averaging of trajectories is investigated. Convergence with p... |
1902.00126 | 2915013032 | We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections.... | A line of work that is known as alternating projections, focus on applying random projections for solving problems that are involving the intersection of infinite number of sets. In particular, these methods focus on the following template Here, the feasible set @math consists of the intersection of a possibly infinite... | {
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"Finding a point in the intersection of a collection of closed convex sets, that is the convex feasibility problem, represents the main modeling strategy for many computational problems. In this paper we analyze new stochastic refo... |
1902.00126 | 2915013032 | We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections.... | Stochastic forward-backward algorithms can also be applied to solve . However, the papers introducing those very general algorithms focused on proving convergence and did not present convergence rates @cite_0 @cite_29 @cite_20 . There are some other works that focus on @cite_2 @cite_10 @cite_25 where the authors assume... | {
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"A stochastic Forward-Backward algorithm with a constant step i... |
1902.00126 | 2915013032 | We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need to be satisfied almost surely. We use smoothing and homotopy techniques to handle constraints without the need for matrix-valued projections.... | Another related work is @cite_15 where the authors apply Nesterov's smoothing to . However, this work does not apply to , due to the Lipschitz continuous assumption on @math . Note that in our main template , @math , which is not Lipschitz continuous. | {
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"In this work we consider the stochastic minimization of nonsmooth convex loss functions, a central problem in machine learning. We propose a novel algorithm called Accelerated Nonsmooth Stochastic Gradient Descent (ANSGD), which e... |
1902.00297 | 2914814133 | Higher inductive-inductive types (HIITs) generalize inductive types of dependent type theories in two ways. On the one hand they allow the simultaneous definition of multiple sorts that can be indexed over each other. On the other hand they support equality constructors, thus generalizing higher inductive types of homo... | The article of @cite_12 gives specification and semantics of QIITs in a set-truncated setting. Signatures are given as lists of functors which can be interpreted as complete categories of algebras, and completeness is used to talk about notions of induction and recursion. However, no strict positivity restriction is gi... | {
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"abstract": [
"Higher inductive types (HITs) in Homotopy Type Theory allow the definition of datatypes which have constructors for equalities over the defined type. HITs generalise quotient types, and allow to define types with non-trivial highe... |
1902.00297 | 2914814133 | Higher inductive-inductive types (HIITs) generalize inductive types of dependent type theories in two ways. On the one hand they allow the simultaneous definition of multiple sorts that can be indexed over each other. On the other hand they support equality constructors, thus generalizing higher inductive types of homo... | Closely related to the current work is the paper by the current authors and Altenkirch @cite_1 , which also concerns QIITs. There, signatures for QIITs are essentially a restriction of the signatures given here, but in contrast to the current work, the restricted quotient setting enables building initial algebras and d... | {
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"abstract": [
"Quotient inductive-inductive types (QIITs) generalise inductive types in two ways: a QIIT can have more than one sort and the later sorts can be indexed over the previous ones. In addition, equality constructors are also allowed. W... |
1902.00297 | 2914814133 | Higher inductive-inductive types (HIITs) generalize inductive types of dependent type theories in two ways. On the one hand they allow the simultaneous definition of multiple sorts that can be indexed over each other. On the other hand they support equality constructors, thus generalizing higher inductive types of homo... | The logical predicate syntactic translation was introduced by @cite_13 . The idea that a context can be seen as a signatures and the logical predicate translation can be used to derive the types of induction motives and methods was described in [Section 5.3] ttintt . Logical relations are used to derive the computation... | {
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"abstract": [
"Reynolds' theory of relational parametricity captures the invariance of polymorphically typed programs under change of data representation. Reynolds' original wor... |
1902.00113 | 2913620370 | Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the da... | Multi-Domain Learning (MDL) MDL aims to learn several domains simultaneously using a single model @cite_7 @cite_32 @cite_27 @cite_30 . Depending on the problem, how much data is available per domain, and how similar the domains are, multi-domain learning can improve @cite_30 -- or sometimes worsen @cite_7 @cite_32 @cit... | {
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"abstract": [
"Abstract: In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning... |
1902.00113 | 2913620370 | Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the da... | Neural Network Meta-Learning Learning-to-learn and meta-learning methods have resurged recently, in particular in few-shot recognition @cite_16 @cite_39 @cite_8 , and learning-to-optimize @cite_34 tasks. Despite signifiant other differences in motivation and methodological formalisations, a common feature of these meth... | {
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"We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gr... |
1902.00197 | 2913476238 | Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fail. However, in modern data science settings where a large number of hypothesis tests need to be performed simultaneously, it is rarely used ... | The problem of multiple testing with MC p-values has been studied in the broader statistical literature. Interesting heuristic adaptive algorithms were proposed without formal FDR guarantee @cite_29 @cite_41 ; the latter was developed via modifying Thompson sampling, another MAB algorithm. Asymptotic results were provi... | {
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"It is a common practice to use resampling methods such as the... |
1902.00197 | 2913476238 | Monte Carlo (MC) permutation test is considered the gold standard for statistical hypothesis testing, especially when standard parametric assumptions are not clear or likely to fail. However, in modern data science settings where a large number of hypothesis tests need to be performed simultaneously, it is rarely used ... | There have been works on fast permutation test for GWAS @cite_8 @cite_11 @cite_10 @cite_17 ; they consider a different goal which is to accelerate the process of separately computing each MC p-value. In contrast, AMT accelerates the entire workflow of both computing MC p-values and applying BH on them, where the decisi... | {
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"Motivation: In genome-wide associatio... |
1902.00127 | 2912839841 | Mixed datasets consist of both numeric and categorical attributes. Various K-means-based clustering algorithms have been developed to cluster these datasets. Generally, these algorithms use random partition as a starting point, which tend to produce different clustering results in different runs. This inconsistency of ... | K-means clustering algorithm is a popular clustering algorithm for datasets consisting of numeric attributes because of its low computational complexity @cite_10 . The complexity is linear with respect to the number of data points and scales well for large datasets. It minimizes the optimization function presented in E... | {
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"The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably eff... |
1902.00127 | 2912839841 | Mixed datasets consist of both numeric and categorical attributes. Various K-means-based clustering algorithms have been developed to cluster these datasets. Generally, these algorithms use random partition as a starting point, which tend to produce different clustering results in different runs. This inconsistency of ... | k-Harmonic means clustering algorithm addresses the random initial clusters problem by using a different cost function @cite_17 for numeric datasets. K-Harmonic means clustering algorithm clusters create more stable clusters as compared to K-means clustering algorithm with random initial clusters. Ahmad and Hashmi @cit... | {
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"Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data w... |
1902.00245 | 2914121279 | Typical recommender systems push K items at once in the result page in the form of a feed, in which the selection and the order of the items are important for user experience. In this paper, we formalize the K-item recommendation problem as taking an unordered set of candidate items as input, and exporting an ordered l... | The position bias is also an important and practical issue in RS. Click models are widely studied in Information Retrieval systems, such as the Cascade Click Model( @cite_29 ) and the Dynamic Bayesian Network( @cite_8 ). It is found that the position bias is not only related to the user's personal habit, but also relat... | {
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"Search engine click logs provide an invaluable source of relevance information, but this information is biased. A key source of bias is presentation order: the pro... |
1902.00245 | 2914121279 | Typical recommender systems push K items at once in the result page in the form of a feed, in which the selection and the order of the items are important for user experience. In this paper, we formalize the K-item recommendation problem as taking an unordered set of candidate items as input, and exporting an ordered l... | We also borrow ideas from applying Reinforcement Learning to Combinatorial Optimization(CO) problems. The pointer network @cite_31 is proposed as neural tool for universal CO. @cite_15 further extended pointer-network by using policy gradients for learning. @cite_35 applied Q-Learning to CO problems on graphs. The afor... | {
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"The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-err... |
1902.00245 | 2914121279 | Typical recommender systems push K items at once in the result page in the form of a feed, in which the selection and the order of the items are important for user experience. In this paper, we formalize the K-item recommendation problem as taking an unordered set of candidate items as input, and exporting an ordered l... | Some recent works have been addresses long-term rewards in recommender systems. @cite_33 also work on intra-list correlations, which applies policy gradient and Monte Carlo Tree Search(MCTS) to optimize the @math -NDCG in diversified ranking for the global optimum. Other works pursues long-term rewards in inter-list re... | {
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"Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typi... |
1901.11260 | 2912134658 | Many systems have to be maintained while the underlying constraints, costs and or profits change over time. Although the state of a system may evolve during time, a non-negligible transition cost is incured for transitioning from one state to another. In order to model such situations, (ICALP 2014) and (ICALP 2014) int... | It is well known that for the usual Knapsack problem, in the continuous relaxation (variables in @math ), at most one variable is fractional. @cite_11 showed that this can be generalized for @math . | {
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"Abstract We address a variant of the classical knapsack problem in which an upper bound is imposed on the number of items that can be selected. This problem arises in the solution of real-life cutting stock problems by column gene... |
1901.11260 | 2912134658 | Many systems have to be maintained while the underlying constraints, costs and or profits change over time. Although the state of a system may evolve during time, a non-negligible transition cost is incured for transitioning from one state to another. In order to model such situations, (ICALP 2014) and (ICALP 2014) int... | @cite_11 use the result of Theorem to show that for any fixed constant @math @math admits a polynomial time approximation scheme (PTAS). Other PTASes have been presented in @cite_18 @cite_15 . Korte and Schrader @cite_6 showed that there is no FPTAS for @math unless @math . | {
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"A fully polynomial time approximation scheme (FPTAS) is presented for the classical 0-1 knapsack problem. The new approach considerably imp... |
1901.11383 | 2952973608 | One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. Thes... | There exists very limited work on digitizing the content of engineering diagrams to facilitate fast and efficient extraction of information. The authors @cite_14 automated the assessment of AutoCAD Drawing Exchange Format (DXF) by converting DXF file into SVG format and developing a marking algorithm of the generated S... | {
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"A spatial relation graph (SRG) and its partial matching method are proposed for online composite graphics representation and recog... |
1901.11383 | 2952973608 | One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. Thes... | However, we discovered a significant body of work on recognition of symbols in prior art. @cite_19 proposed Fourier Mellin Transform features to classify multi-oriented and multi-scaled patterns in engineering diagrams. Other models utilized for symbol recognition include Auto Associative neural networks @cite_4 , Deep... | {
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"Symbol recognition is a well-known problem in the field of graphics. A ... |
1901.11383 | 2952973608 | One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. Thes... | In literature, Connected Component (CC) analysis @cite_18 has been used extensively for extracting characters @cite_13 from images. However, connected components are extremely sensitive to noise and thresholding may not be suitable for P &ID text extraction. Hence, we utilize the recently invented Connectionist Tempora... | {
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"In this paper, we present a new scene text detection algorithm based on two machine learning clas... |
1901.11417 | 2913832161 | Fluid approximations have seen great success in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the continuous-time Markov chain (CTMC) having a particular population structure which suggests a natural continuous state-space endowed with a... | In the case of pCTMCs, a more concise description in terms of the collective dynamics of population averages is however available. Starting with the seminal work of van Kampen @cite_0 , and motivated by the interpretation of pCTMCs as chemical reaction systems, several approximation schemes have been developed which re... | {
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"In order to solve the master equation by a systematic approximation method, an expansion in powers of some parameter is needed. The appropriate parameter is the reciprocal size of the system, define... |
1901.11417 | 2913832161 | Fluid approximations have seen great success in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the continuous-time Markov chain (CTMC) having a particular population structure which suggests a natural continuous state-space endowed with a... | Following Darling and Norris @cite_17 , we examine and formalise the aspects of pCTMCs which render them especially amenable to the fluid approximation. As mentioned, the first is that pCTMC state-spaces are countable and there exists an obvious ordering. We can therefore write a trivial linear mapping from the discret... | {
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"We formulate some simple conditions under which a Markov chain may be approximated by the solution to a differential equation, with quantifiable error probabilities. The role of a choice of coordinate functions for the Markov chai... |
1901.11417 | 2913832161 | Fluid approximations have seen great success in approximating the macro-scale behaviour of Markov systems with a large number of discrete states. However, these methods rely on the continuous-time Markov chain (CTMC) having a particular population structure which suggests a natural continuous state-space endowed with a... | There are many ways to satisfy the above criteria, but a common one (used in pCTMCs) is hydrodynamic scaling'', where the increments of the @math -state Markov process mapped to the Euclidean space are @math and the jump rate is @math . The criteria above are derived formally in @cite_17 @cite_18 and ensure that: | {
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"A rescaled Markov chain converges uniformly in probability to the solution of an ordinary differential equation, under carefully specified assumptions. The presentation is much simpler than those ... |
1901.11344 | 2914116739 | Recent years has witnessed dramatic progress of neural machine translation (NMT), however, the method of manually guiding the translation procedure remains to be better explored. Previous works proposed to handle such problem through lexcially-constrained beam search in the decoding phase. Unfortunately, these lexicall... | The establishment of one efficient and effective machine translation system is attractive over the decades. Although systems based on statistical machine translation have been used in real life, the unpromising performance makes it difficult to be promoted. Recent works of neural machine translation have made this poss... | {
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"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention m... |
1901.11344 | 2914116739 | Recent years has witnessed dramatic progress of neural machine translation (NMT), however, the method of manually guiding the translation procedure remains to be better explored. Previous works proposed to handle such problem through lexcially-constrained beam search in the decoding phase. Unfortunately, these lexicall... | External memory has been used in several works @cite_2 @cite_0 @cite_3 to enhance the quality of neural machine translation. For example, proposes to extract phrase table as recommendation memory for neural machine translation. However, this kind of phrase table is too noisy, which is also mentioned in . proposes to st... | {
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"Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has lim... |
1901.11259 | 2913092861 | Convolutional neural networks have been widely used in content-based image retrieval. To better deal with large-scale data, the deep hashing model is proposed as an effective method, which maps an image to a binary code that can be used for hashing search. However, most existing deep hashing models only utilize fine-le... | DSPH @cite_13 firstly proposes to utilize the pair-wise label to train the end-to-end deep hashing model. HashNet @cite_0 defines a weighted maximum likelihood of pairwise logistic for balance the similar and dissimilar labels. DTSH @cite_22 extends the pair-wise supervision to the triplet one for more effectively capt... | {
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"Hashing is one of the most ... |
1901.11259 | 2913092861 | Convolutional neural networks have been widely used in content-based image retrieval. To better deal with large-scale data, the deep hashing model is proposed as an effective method, which maps an image to a binary code that can be used for hashing search. However, most existing deep hashing models only utilize fine-le... | Recently, the semantic hierarchy learning problem has been addressed in several works. The hierarchical semantic image retrieval model is proposed in @cite_14 . This work encodes hierarchy in semantic similarity. By combining the coarse and fine level labels, work in @cite_18 proves that the image classification perfor... | {
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"abstract": [
"The performance of classifiers is in general improved by desi... |
1901.11448 | 2952848538 | The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear... | Multi-Domain Learning (MDL) MDL addresses training a single model capable of solving multiple datasets (domains). If the data is relatively small and the domains are similar, this sharing can lead to improved performance compared to training a separate model per domain @cite_25 . On the other hand, for diverse domains ... | {
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"There is a growing interest in learning data representations that work well for many different types of problems and data. In this paper, we look in particular at the task ... |
1901.11448 | 2952848538 | The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear... | Most existing DG approaches can be split into three categories: feature-based methods, classifier-based methods, and data augmentation methods. Feature-based methods: These aim to generate a domain-invariant representation. For example where the distance between the empirical distributions of the source and target exam... | {
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1901.11448 | 2952848538 | The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear... | Few studies have considered the heterogeneous DG setting, where the domains do not share the same label space. In this setting, we do not expect the to generalize directly to the target domain (impossible due to the change in label space), but we do aim to improve the robustness of a source-domain trained in terms of i... | {
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1901.11448 | 2952848538 | The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear... | Meta-Learning Meta-learning (a.k.a. learning to learn, @cite_15 @cite_13 ) has received resurgence in interest recently with applications in few-shot learning @cite_9 @cite_40 and beyond @cite_16 . In few-shot meta-learning, a common strategy is to simulate the few-shot learning scenario by randomly drawing few-shot tr... | {
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1901.11448 | 2952848538 | The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of lear... | A few methods have applied related episodic meta-learning strategies in DG @cite_2 @cite_8 . MLDG @cite_2 defined a heuristic gradient descent update rule based on the gradients of the simulated training and testing domains. MetaReg @cite_8 trains the weights of the 's regulariser so as to produce a more general classi... | {
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"We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given... |
1901.11462 | 2952666417 | Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world. This paper investigates the task of building a non-goal driven conversational agent, using neural network generative models and analyzes how the conversation context is handled. It compa... | The traditional approach for Conversational Agents follows a modular approach, dividing the process into three modules: a Natural Language Understanding (NLU) unit, a Dialogue Manager and a Natural Language Generation module (NLG). The NLU module will process the input and extract useful information. This information i... | {
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"The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of... |
1901.11462 | 2952666417 | Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world. This paper investigates the task of building a non-goal driven conversational agent, using neural network generative models and analyzes how the conversation context is handled. It compa... | The use of Encoder-Decoder architectures for natural language processing was first proposed as a solution for text translation, in 2014 by @cite_7 . From then on, the architecture has been applied to many other tasks, including conversational agents @cite_6 . However, generating responses was found to be considerably m... | {
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"Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an ef... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | As might be expected, filter-based FS algorithms have asymptotic complexities that depend on the number of features and or instances in a dataset. Many algorithms, such as the CFS, have quadratic complexities, while the most frequently used algorithms have at least linear complexities @cite_26 . This is why, in recent ... | {
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"The explosion of big data has posed important challenges to researchers.Feature selection is paramount when dealing with high-dimensional datasets.We review the state-of-the-art and recent contributions in feature selection.The eme... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | @cite_8 developed parallel versions of three forward-search-based FS algorithms, where a wrapper with a logistic regression classifier is used to guide a search parallelized using the MapReduce model. | {
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"abstract": [
"The set of features used by a learning algorithm can have a dramatic impact on the performance of that algorithm. Including extraneous features can make the learning problem harder by adding useless, noisy dimensions that lead to o... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | @cite_12 addressed the FS scaling problem using an asynchronous search approach, given that synchronous search, as commonly performed, can lead to efficiency losses due to the inactivity of some processors waiting for other processors to end their tasks. In their tests, they first obtained an initial reduction using a ... | {
"cite_N": [
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"abstract": [
"Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency ... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | @cite_23 used the MapReduce model to implement a wrapper-based evolutionary search FS method. The dataset was split by instances and the FS method was applied to each resulting subset. Simple majority voting was used as a reduction step for the selected features and the final subset of feature was selected according to... | {
"cite_N": [
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"abstract": [
"Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classifi... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | Bol 'on- @cite_39 proposed a framework to deal with high dimensionality data by first optionally ranking features using a FS filter, then partitioning vertically by dividing the data according to features (columns) rather than, as commonly done, according to instances (rows). After partitioning, another FS filter is ap... | {
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1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | @cite_24 described a distributed parallel FS method based on a variance preservation criterion using the proprietary software SAS High-Performance Analytics. http: www.sas.com en_us software high-performance-analytics.html One remarkable characteristic of the method is its support not only for supervised FS, but also f... | {
"cite_N": [
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"abstract": [
"Advances in computer technologies have enabled corporations to accumulate data at an unprecedented speed. Large-scale business data might contain billions of observations and thousands of features, which easily brings their scale ... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | Ram 'irez- @cite_28 described scalable versions of the popular mRMR @cite_9 FS filter that included a distributed version using Spark. The authors showed that their version that leveraged the power of a cluster of computers could perform much faster than the original and processed much larger datasets. | {
"cite_N": [
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"abstract": [
"With the advent of large-scale problems, feature selection has become a fundamental preprocessing step to reduce input dimensionality. The minimum-redundancy-maximum-relevance (mRMR) selector is co... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | In a previous work @cite_36 , using the Spark computing model we designed a distributed version of the ReliefF @cite_13 filter, called DiReliefF. In testing using datasets with large numbers of features and instances, it was much more efficient and scalable than the original filter. | {
"cite_N": [
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"abstract": [
"Feature selection (FS) is a key research area in the machine learning and data mining fields; removing irrelevant and redundant features usually helps to reduce the effort required to process a da... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | Finally, Eiras- @cite_32 , using four distributed FS algorithms, three of them filters, namely, InfoGain @cite_6 , ReliefF @cite_13 and the CFS @cite_33 , reduce execution times with respect to the original versions. However, in the CFS case, the version of those authors focuses on regression problems where all the fea... | {
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"In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and... |
1901.11286 | 2899274556 | Abstract Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed versi... | The approach described here can be categorized as a approach that builds on works described elsewhere @cite_28 , @cite_36 , @cite_32 . The fact that their focus was not only on designing an efficient and scalable FS algorithm, but also on preserving the original behaviour (and obtaining the same final results) of tradi... | {
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"abstract": [
"With the advent of large-scale problems, feature selection has become a fundamental preprocessing step to reduce input dimensionality. The minimum-redundancy-maxi... |
1901.11524 | 2950533301 | We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (, 2010). To demonstrate this result, we exhibit several properties of the structural relations... | The dual formulation consists of maximizing the expected return for a given initial state distribution, as a function of the discounted state action visit frequency distribution. Contrary to the primal form, any feasible discounted state action visit frequency distribution maps to an policy @cite_9 . | {
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"We investigate the dual approach to dynamic programming and reinforcement learning, based on maintaining an explicit representation of stationary distributions as opposed to value functions. A significant advantage of the dual appr... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | Chase and Kamara @cite_19 introduced the notion of graph encryption while they were presenting structured encryption as a generalization of searchable symmetric encryption (SSE) proposed by Song @cite_23 . They presented schemes for , and on labeled graph-structured data. In all of their proposed schemes, the graph was... | {
"cite_N": [
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"abstract": [
"We consider the problem of encrypting structured data (e.g., a web graph or a social network) in such a way that it can be efficiently and privately queried. For this purpose, we introduce the not... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | A parallel secure computation framework has been designed and implemented by Nayak @cite_12 . This framework computes functions like histogram, PageRank, matrix factorization etc. To run this algorithms, introduced parallel programming paradigms to secure computation. The parallel and secure execution enables the algor... | {
"cite_N": [
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"abstract": [
"We present Path ORAM, an extremely simple Oblivious RAM protocol with a small amount of client storage. Partly due to its simplicity, Path ORAM is the most practical ORAM scheme known to date with... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | Sketch-based approximate shortest distance queries over encrypted graph have been studied by Meng @cite_16 . In the pre-processing stage, the client computes the sketches for every vertex that is useful for efficient shortest distance query. Instead of encrypting the graph directly, they encrypted the pre-processed dat... | {
"cite_N": [
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"abstract": [
"We propose graph encryption schemes that efficiently support approximate shortest distance queries on large-scale encrypted graphs. Shortest distance queries are one of the most fundamental graph operations and have a wide range o... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | Shen @cite_1 introduced and studied cloud-based approximate in encrypted graphs which finds the shortest distance with a constraint such that the total cost does not exceed a given threshold. | {
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"mid": [
"2770638201"
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"abstract": [
"Constrained shortest distance (CSD) querying is one of the fundamental graph query primitives, which finds the shortest distance from an origin to a destination in a graph with a constraint that the total cost does not exceed a giv... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | Exact distance has been computed on dynamic encrypted graphs in @cite_9 . Similar to our paper, this paper uses a proxy to reduce client-side computation and information leakage to the cloud. In the scheme, adjacency lists are stored in an inverted index. However, in a single query, the scheme leaks all the nodes reach... | {
"cite_N": [
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"abstract": [
"In the era of big data, graph databases have become increasingly important for NoSQL technologies, and many systems can be modeled as graphs for semantic queries. Meanwhile, with the advent of cloud computing, data owners are highl... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | A graph encryption scheme, that supports top- @math nearest keyword search queries, has been proposed by Liu @cite_13 . They have made an encrypted index using order preserving encryption for searching. Together with lightweight symmetric key encryption schemes, homomorphic encryption is used to compute on encrypted da... | {
"cite_N": [
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"mid": [
"2614426900"
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"abstract": [
"Driven by the growing security demands of data outsourcing applications in sustainable smart cities, encrypting clients’ data has been widely accepted by academia and industry. Data encryptions should be done at the client side be... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | Besides, Zheng @cite_11 proposed link prediction in decentralized social network preserving the privacy. Their construction split the link score into private and public parts and applied sparse logistic regression to find links based on the content of the users. However, the graph data was not considered to be encrypte... | {
"cite_N": [
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"mid": [
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"abstract": [
"We consider the privacy-preserving link prediction problem in decentralized online social network (OSNs). We formulate the problem as a sparse logistic regression problem and solve it with a novel decentralized two-tier method usi... |
1901.11308 | 2914465018 | Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers w... | In this paper, we outsource the graph in encrypted form. In most of the previous works, the schemes are designed to perform single specific query like neighbor query ( @cite_19 ), shortest distance query ( @cite_16 @cite_1 @cite_9 ), focused subgraph queries ( @cite_19 ) etc. So, either it is hard to get the informatio... | {
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"abstract": [
"We consider the problem of encrypting structured data (e.g., a web graph or a social network) in such a way that it can be efficie... |
1901.11267 | 2949631225 | Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons ... | @cite_8 shows an approach very similar to ours: Based on a survey of lifecycle models, an abstract data lifecycle model is derived and a classification scheme is developed. In contrast to @cite_8 , we do not define a lifecycle model but a common scheme shared by all found lifecycle models. One of features by which @cit... | {
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"abstract": [
"The Semantic Web, especially in the light of the current focus on its nature as a Web of Data, is a data-centric system, and arguably the largest such system in existence. Data is being created, published, exported, imported, used,... |
1901.11267 | 2949631225 | Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons ... | @cite_10 , @cite_1 and @cite_7 are alike to @cite_8 in the approach to review existing models and deriving an own lifecycle model based on a gap analysis. None of the three publications offer generic and empirical evaluation criteria or a metamodel for the existing models. Their lifecycle model is designed to supersede... | {
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"abstract": [
"A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other fie... |
1901.11267 | 2949631225 | Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons ... | @cite_1 and @cite_7 both propose a lifecycle model for Big Data. Although they model the same phenomena, the models are not similar. While @cite_7 does not describe evaluation criteria of the model, @cite_1 proposes the 6Vs of Big Data (Value, Volume, Variety, Velocity, Variability, Veracity) as a base to evaluate data... | {
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"abstract": [
"A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are... |
1901.11267 | 2949631225 | Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons ... | @cite_20 provides a scoped review of 301 articles and 10 companion documents discussing research data management practices in academic institutions between 1995 and 2016. The review is not limited to, but includes publications discussing data lifecycle models. The discussion includes the observation, that of the papers... | {
"cite_N": [
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"abstract": [
"Objective The purpose of this study is to describe the volume, topics, and methodological nature of the existing research literature on research data management in academic institutions. Materials and methods We conducted a scopin... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | In the absence of external parallel data, one polylingual DSM which has recently proved worthy (and that we use as a baseline in our experiments) is (LRI -- @cite_2 ), the polylingual extension of the (RI) method @cite_22 . RI is a context-counting model belonging to the family of random projection methods, and is cons... | {
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"abstract": [
"Word space models enjoy considerable attention in current research on semantic indexing. Most notably, Latent Semantic Analysis Indexing (LSA LSI; , 1990, Landauer ... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | Another method that requires external multilingual resources (specifically: a word translation oracle) is (CL-SCL -- @cite_39 ). CL-SCL relies on solving auxiliary prediction problems, which consist in discovering hidden correlations between terms in a language. This is achieved by binary classifiers trained to predict... | {
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"abstract": [
"Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a \"target\" domain w... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | Approaches to PLC based on deep learning focus on defining representations based on word embeddings which capture the semantic regularities in language while at the same time being aligned across languages. In order to produce aligned representations, though, deep learning approaches typically require the availability ... | {
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1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | Funnelling is reminiscent of the (a.k.a. stacking'') method for ensemble learning @cite_36 . Let us discuss their commonalities and differences. | {
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"This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided le... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | Common to stacking and funnelling is the presence of an ensemble of @math base classifiers, typically trained on traditional'' vectorial representations, and the presence of a single meta-classifier that operates on vectors of base-classifier outputs. Common to stacking and is also the use of @math -fold cross-validati... | {
"cite_N": [
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"We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e- mail, or \"sp... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | However, a key difference between the two methods is that stacking (like other ensemble methods such as bagging @cite_10 and boosting @cite_35 ) deals with ( homogeneous'') scenarios in which all training documents can in principle be represented in the same feature space and can thus concur to training the same classi... | {
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"abstract": [
"In an earlier paper, we introduced a new \"boosting\" algorithm called AdaBoost which, theoretically, ca... |
1901.11459 | 2963967134 | Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increa... | It certainly that exist in multilabel settings @cite_28 @cite_5 @cite_18 , which is not possible when (as customarily done) a multilabel classification task is solved as @math independent binary classification problems. In fact, for an unlabelled document @math the meta-classifier receives @math inputs from the base cl... | {
"cite_N": [
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"abstract": [
"In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines ... |
1901.11382 | 2950001871 | In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, wat... | Generative adversarial Network (GAN) @cite_6 is the idea that has taken deep learning by storm. It employs adversarial training which essentially means pitting two neural networks against each other. One is a generator while the other is a discriminator, where the former aims at producing data that are indistinguishabl... | {
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1901.11382 | 2950001871 | In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, wat... | Image-to-image translation is the task of mapping images in source domain to images in target domain such as converting sketches into photographs, grayscale images to color images etc. The aim is to generate the target distribution given the source distribution. Prior work in the field of GANs such as Conditional GAN @... | {
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"Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set o... |
1901.11382 | 2950001871 | In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents and courier receipts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as background noise, blur due to camera motion, wat... | Very few attempts have been made in past for removing watermarks from images. Authors in @cite_15 proposed to use image inpainting to recover the original image. However, the method developed by @cite_24 detects the watermark using statistical methods and subsequently, removes it using image inpainting. To the best of ... | {
"cite_N": [
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"abstract": [
"This paper introduces a technique to remove visible watermark automatically using image inpainting algorithms. The pending images which need watermark re-moval are assumed to have same resolution ... |
1901.11461 | 2949409558 | Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue tha... | Mesh models have only recently been used in generation and reconstruction tasks due to the challenging nature of their complex definition @cite_23 . Recent mesh approaches rely on graph representations of meshes, and use GCNs @cite_28 to effectively process them. Our work most closely relates to Neural 3D Mesh Renderer... | {
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1901.11461 | 2949409558 | Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue tha... | The great success of convolutional neural networks in numerous image-based tasks @cite_37 @cite_15 @cite_14 @cite_2 @cite_61 has led to increasing efforts to extend deep networks to domains where graph-structured data is ubiquitous. | {
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"abstract": [
"State-of-the-art semantic segmentation approaches increase the receptive field of their models by using e... |
1901.11461 | 2949409558 | Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue tha... | Early attempts to extend neural networks to deal with arbitrarily structured graphs relied on recursive neural networks @cite_17 @cite_64 @cite_34 . Recently, spectral approaches have emerged as an effective alternative which formulates the convolution as an operation on the spectrum of the graph @cite_18 @cite_59 @cit... | {
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1901.10997 | 2913686213 | Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promising for cutting down network complexity by skipping insignificant weights. However, current compression strategies are mostly hardware-agnosti... | Various attempts have been made to improve the efficiency of LSTM models. One direction focuses on improving the LSTM cells. The gated recurrent unit (GRU) utilizes reset and update gates to achieve a similar performance to an LSTM while reducing computational cost @cite_5 . Quasi-RNN explores the intrinsic parallelism... | {
"cite_N": [
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"abstract": [
"Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performanc... |
1901.10997 | 2913686213 | Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promising for cutting down network complexity by skipping insignificant weights. However, current compression strategies are mostly hardware-agnosti... | Network compression techniques, such as the grow-and-prune paradigm, have recently emerged as another direction for reducing LSTM redundancy. The pruning method was initially shown to be effective on large CNNs by demonstrating the reduction in the number of parameters in AlexNet by 9 @math and VGG by 13 @math for the ... | {
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"abstract": [
"Recurrent Neural Networks (RNN) are widely used to solve a variety of p... |
1901.11168 | 2972498781 | The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of hea... | With the rise of unsupervised deep learning models especially auto-encoders @cite_19 and their great performance in other domains such as image recognition @cite_1 , their application has recently emerged in wireless health for detection of anomalies in health signals such as ECG signals. @cite_7 @cite_14 are among stu... | {
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"",
"Since security threats to WSNs a... |
1901.11168 | 2972498781 | The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of hea... | LSTM auto-encoders @cite_0 were later introduced in learning representations of videos and improved feature extraction by capturing temporal features of the signal. They were later used in time series analysis as well @cite_17 . Moreover, Two recent studies have shown improved performance of auto-encoders in more compl... | {
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"We use Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map a... |
1901.11168 | 2972498781 | The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of hea... | Prediction of Bradycardia in infants using the PICS dataset was approached before by publishers of the dataset with statistical methods @cite_2 . They specifically used a point process analysis and tried to capture the differences in variance and mean of signal segments before a Bradycardia event. Although this study p... | {
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"Objective: Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabiliz... |
1901.11220 | 2912240042 | Initial access (IA) is a fundamental procedure in cellular systems where user equipment (UE) detects base station (BS) and acquires synchronization. Due to the necessity of using antenna arrays for IA in millimeter-wave (mmW) systems, BS simultaneously performs beam training to acquire angular channel state information... | The alternative approaches for beam training are based on parametric channel estimation @cite_31 @cite_24 @cite_32 @cite_18 @cite_25 @cite_17 @cite_42 @cite_45 . Exploiting the mmW sparse scattering nature, compressive sensing (CS) approaches have been considered to effectively estimate channel parameters based on chan... | {
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1901.11220 | 2912240042 | Initial access (IA) is a fundamental procedure in cellular systems where user equipment (UE) detects base station (BS) and acquires synchronization. Due to the necessity of using antenna arrays for IA in millimeter-wave (mmW) systems, BS simultaneously performs beam training to acquire angular channel state information... | There are also recent works that consider some practical aspects of IA. For example, frequency offset robust algorithms in narrowband mmW beam training are reported in @cite_6 @cite_40 @cite_29 . There are several hardware prototypes that consider a practical approach of using received signal strength (RSS) in CS-based... | {
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"abstract": [
"Millimeter (mm) wave massive MIMO has the potential for delive... |
1901.11188 | 2952085755 | The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research. It has been shown that both neural networks and adversarial attacks against them can be sensitive to input transformations such as linear translation and rotation, and that human vision, which is... | @cite_25 proposed using random transformations to pre-processing the input images to improve model robustness. It was later shown, however, that this approach creates a gradient masking effect and can be broken by robust attacks @cite_42 . Unlike @cite_25 , we consider the transformation as part of our model during the... | {
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"We identify obfuscated gradients as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat o... |
1901.11188 | 2952085755 | The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research. It has been shown that both neural networks and adversarial attacks against them can be sensitive to input transformations such as linear translation and rotation, and that human vision, which is... | Attacks from an ensemble of black-box models have been used to effectively avoid gradient masking in one-step adversarial training @cite_28 . While our model also uses an ensemble of attacks, these attacks are white-box and multi-step. Importantly, these attacks do not cause gradient masking. | {
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"Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are... |
1901.11167 | 2952180917 | In the literature, tensors have been effectively used for capturing the context information in language models. However, the existing methods usually adopt relatively-low order tensors, which have limited expressive power in modeling language. Developing a higher-order tensor representation is challenging, in terms of ... | There have been tremendous research efforts in the field of statistical language modeling. Some earlier language models are based on the Markov assumption are represented by @math -gram models @cite_17 , where the prediction of the next word is often conditioned just on @math preceding words. For @math -gram models, Kn... | {
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1901.10951 | 2914704920 | For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using... | Object detection has been a major topic of computer vision research and over recent years a number of fully convolutional object detectors have been proposed. Two-stage methods, in particular Faster R-CNN @cite_2 , provide state-of-the-art performance but are computationally expensive. One-stage methods @cite_17 @cite_... | {
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"The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied... |
1901.10951 | 2914704920 | For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using... | A common approach when training object detectors for a specific task is to pre-train a feature extractor using ImageNet @cite_18 and then fine tune the features with the limited training data available for the task. In @cite_10 Shen show that, given careful network design, it is possible to obtain state of the art resu... | {
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1901.10951 | 2914704920 | For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using... | A number of papers use automated methods for generating training labels. In @cite_1 , visual odometry from previous traversals is used to label driveable surfaces for semantic segmentation. Hoermann @cite_11 employs temporal consistency to generate labels by processing data both forwards and backwards in time. Recent w... | {
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"abstract": [
"We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using re... |
1901.10951 | 2914704920 | For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using... | Multi-modal object detection has been investigated in a number of works. In @cite_4 camera images are combined with both frontal and birds eye LIDAR views for 3D object detection. Data from cameras, LIDAR and radar are all fused in @cite_19 . | {
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1901.11117 | 2952355681 | Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models. Our goal is to apply NAS to search for a better alternative to the Transformer. We first construct a large search space in... | RNNs have long been used as the default option for applying neural networks to sequence modeling @cite_16 @cite_31 , with LSTM @cite_10 and GRU @cite_20 architectures being the most popular. However, recent work has shown that RNNs are not necessary to build state-of-the-art sequence models. For example, many high perf... | {
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1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | Go-Explore is reminiscent of earlier work that separates exploration and exploitation (e.g. ), in which exploration follows a reward-agnostic Goal Exploration Process @cite_42 (an algorithm similar to novelty search @cite_46 ), from which experience is collected to prefill the replay buffer of an off-policy RL algorith... | {
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1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | The aspect of Go-Explore of first finding a solution and then robustifying around it has precedent in Guided Policy Search @cite_19 . However, this method requires a non-deceptive, non-sparse, differentiable loss function to find solutions, meaning it cannot be applied directly to problems where rewards are discrete, s... | {
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1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | The idea of planning (searching in a deterministic model of the world to find a good strategy) and then training a policy to mimic what was learned is reminiscent of . It plans (in the Atari emulator) with UCT @cite_100 @cite_22 @cite_73 , which is slow, and then trains a much faster policy with supervised learning to ... | {
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"For large state-space Markovian Decision Problems Monte-Carlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introd... |
1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | UCT plans in a model of the world so as to decide on the next action to take in the real environment. An exploration bonus is used during the planning phase, but only extrinsic rewards are considered when choosing the next action to take. This approach can improve performance in domains with relatively dense rewards, b... | {
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1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | Another approach to planning is Fractal Monte Carlo (FMC) @cite_78 . When choosing the next action, it takes into account both the expected reward and novelty of that action, and in that way is more similar to Go-Explore. In FMC, a planning process is initiated from each state the agent visits. Planning is done within ... | {
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1901.10995 | 2914261249 | A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic... | On Pitfall, SOORL @cite_51 was the first planning algorithm to achieve a non-zero score, but did so in a deterministic test environment. It does so through a combination of learning a model of the environment, domain knowledge, and a value function that is optimistic about the value of unseen states, thus effectively p... | {
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"abstract": [
"Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic exploration. Inspi... |
1901.10912 | 2914607694 | We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster ad... | Approaches for Bayesian network structure learning based on discrete search over model structures and simulated annealing are reviewed in . There, it has been common to use Minimum Description Length (MDL) principles to score and search over models , or the Bayesian Information Criterion (BIC) to search for models with... | {
"cite_N": [
"@cite_5",
"@cite_1",
"@cite_3",
"@cite_2"
],
"mid": [
"2049910836",
"",
"2466989778",
"2170112109"
],
"abstract": [
"SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and s... |
1907.10471 | 2962991329 | We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw point cloud as input to generate accurate proposals by seeding each point with a new spherical anchor. It achieves a high recall with less co... | There are several approaches to tackle semantic segmentation on point cloud. In @cite_5 , a projection function converts LiDAR points to a UV map, which is then classified by 2D semantic segmentation @cite_5 @cite_7 @cite_3 in pixel level. In @cite_11 @cite_21 , a multi-view-based function produces the segmentation mas... | {
"cite_N": [
"@cite_4",
"@cite_7",
"@cite_21",
"@cite_1",
"@cite_3",
"@cite_6",
"@cite_27",
"@cite_2",
"@cite_5",
"@cite_11"
],
"mid": [
"",
"2560023338",
"2594519801",
"2560609797",
"2412782625",
"2810641456",
"2963719584",
"2963121255",
... |
1907.10471 | 2962991329 | We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw point cloud as input to generate accurate proposals by seeding each point with a new spherical anchor. It achieves a high recall with less co... | For multi-view methods, MV3D @cite_0 projects LiDAR point cloud to BEV and trains a Region Proposal Network (RPN) to generate positive proposals. It merges features from BEV, image view and front view in order to generate refined 3D bounding boxes. AVOD @cite_18 improves MV3D by fusing image and BEV features like @cite... | {
"cite_N": [
"@cite_0",
"@cite_18",
"@cite_33"
],
"mid": [
"2555618208",
"2774996270",
"2565639579"
],
"abstract": [
"This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that take... |
1907.10599 | 2963790895 | Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will stu... | This signal propagation perspective can be refined via random matrix theory @cite_25 @cite_24 . In these works, free probability is leveraged to compute the singular value distribution of the input-output map given by the random neural network, as the input dimension and width tend to infinity together. Other works als... | {
"cite_N": [
"@cite_24",
"@cite_25"
],
"mid": [
"2789210533",
"2963570896"
],
"abstract": [
"Recent work has shown that tight concentration of the entire spectrum of singular values of a deep network's input-output Jacobian around one at initialization can speed up learning by orders of m... |
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