4
4.0
Jun 30, 2018
06/18
by
Oscar Stiffelman
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The true process that generated data cannot be determined when multiple explanations are possible. Prediction requires a model of the probability that a process, chosen randomly from the set of candidate explanations, generates some future observation. The best model includes all of the information contained in the minimal description of the data that is not contained in the data. It is closely related to the Halting Problem and is logarithmic in the size of the data. Prediction is difficult...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1404.0789
3
3.0
Jun 30, 2018
06/18
by
Jonathan D. Gammell; Siddhartha S. Srinivasa; Timothy D. Barfoot
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Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature. For problems seeking to minimize path length, the subset of states that can...
Topics: Robotics, Computing Research Repository
Source: http://arxiv.org/abs/1404.2334
2
2.0
Jun 30, 2018
06/18
by
Cong Li; Michael Georgiopoulos; Georgios C. Anagnostopoulos
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A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL)...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1404.3190
2
2.0
Jun 30, 2018
06/18
by
Athanasios Krontiris; Rahul Shome; Andrew Dobson; Andrew Kimmel; Issac Yochelson; Kostas Bekris
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This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a factory floor in a desirable arrangement or for retrieving a particular object from a shelf blocked by similarly sized objects. These are challenging problems as they involve combinatorially large, continuous configuration spaces due to the presence of multiple moving bodies and kinematically complex manipulators....
Topics: Robotics, Computing Research Repository
Source: http://arxiv.org/abs/1404.6573
2
2.0
Jun 30, 2018
06/18
by
Arvind Agarwal; Saurabh Kataria
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In this paper, we present a learning method for sequence labeling tasks in which each example sequence has multiple label sequences. Our method learns multiple models, one model for each label sequence. Each model computes the joint probability of all label sequences given the example sequence. Although each model considers all label sequences, its primary focus is only one label sequence, and therefore, each model becomes a task-specific model, for the task belonging to that primary label....
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1404.6580
2
2.0
Jun 30, 2018
06/18
by
Hasan Abasi; Nader H. Bshouty; Hanna Mazzawi
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In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by applications arising in chemical reactions and genome sequencing. We first present new lower bounds for this problem and then present deterministic and randomized adaptive...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1405.0792
3
3.0
Jun 30, 2018
06/18
by
Wilhelmiina Hämäläinen
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An association rule is statistically significant, if it has a small probability to occur by chance. It is well-known that the traditional frequency-confidence framework does not produce statistically significant rules. It can both accept spurious rules (type 1 error) and reject significant rules (type 2 error). The same problem concerns other commonly used interestingness measures and pruning heuristics. In this paper, we inspect the most common measure functions - frequency, confidence, degree...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1405.1360
3
3.0
Jun 29, 2018
06/18
by
Joseph Campbell; Cumhur Erkan Tuncali; Theodore P. Pavlic; Georgios Fainekos
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As autonomous or semi-autonomous vehicles are deployed on the roads, they will have to eventually start communicating with each other in order to achieve increased efficiency and safety. Current approaches in the control of collaborative vehicles primarily consider homogeneous simplified vehicle dynamics and usually ignore any communication issues. This raises an important question of how systems without the aforementioned limiting assumptions can be modeled, analyzed and certified for safe...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1604.02122
2
2.0
Jun 29, 2018
06/18
by
Yun Fei
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In this technical report we derive the analytic form of the Hessian matrix for shape matching energy. Shape matching is a useful technique for meshless deformation, which can be easily combined with multiple techniques in real-time dynamics. Nevertheless, it has been rarely applied in scenarios where implicit integrators are required, and hence strong viscous damping effect, though popular in simulation systems nowadays, is forbidden for shape matching. The reason lies in the difficulty to...
Topics: Graphics, Computing Research Repository
Source: http://arxiv.org/abs/1604.02483
4
4.0
Jun 29, 2018
06/18
by
Raviteja Upadrashta; Tarun Choubisa; A. Praneeth; Tony G.; Aswath V. S.; P. Vijay Kumar; Sripad Kowshik; Hari Prasad Gokul R; T. V. Prabhakar
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This paper presents the development of a passive infra-red sensor tower platform along with a classification algorithm to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was aimed at exploring the potential use of wireless sensor networks as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. There are three important features to the development....
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1604.03829
2
2.0
Jun 29, 2018
06/18
by
Martin Sinclair; Ioannis Raptis
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Large-Scale Actuator Networks (LSAN) are a rapidly growing class of electromechanical systems. A prime application of LSANs in the industrial sector is distributed manipulation. LSAN's are typically implemented using: vibrating plates, air jets, and mobile multi-robot teams. This paper investigates a surface capable of morphing its shape using an array of linear actuators to impose two dimensional translational movement on a set of objects. The collective nature of the actuator network...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1604.04659
2
2.0
Jun 29, 2018
06/18
by
Han-Zhou Wu; Hong-Xia Wang; Yun-Qing Shi
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This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on the...
Topics: Multimedia, Computing Research Repository
Source: http://arxiv.org/abs/1604.04984
2
2.0
Jun 29, 2018
06/18
by
Sayed Hossein Khatoonabadi; Ivan V. Bajic; Yufeng Shan
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Computational modeling of visual saliency has become an important research problem in recent years, with applications in video quality estimation, video compression, object tracking, retargeting, summarization, and so on. While most visual saliency models for dynamic scenes operate on raw video, several models have been developed for use with compressed-domain information such as motion vectors and transform coefficients. This paper presents a comparative study of eleven such models as well as...
Topics: Multimedia, Computing Research Repository
Source: http://arxiv.org/abs/1604.07339
2
2.0
Jun 29, 2018
06/18
by
Karol Hausman; James Preiss; Gaurav Sukhatme; Stephan Weiss
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We study the nonlinear observability of a systems states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Common trajectory-planning algorithms tend to generate motions that lead to an unobservable subspace of the system state, causing suboptimal state...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1604.07905
3
3.0
Jun 29, 2018
06/18
by
Michel Coste; Philippe Wenger; Damien Chablat
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This paper investigates a situation pointed out in a recent paper, in which a non-singular change of assembly mode of a planar 2-RPR-PR parallel manipulator was realized by encircling a point of multiplicity 4. It is shown that this situation is, in fact, a non-generic one and gives rise to cusps under a small perturbation. Furthermore , we show that, for a large class of singularities of multiplicity 4, there are only two types of stable singularities occurring in a small perturbation: these...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1604.08742
2
2.0
Jun 29, 2018
06/18
by
Jan Winkler; Ferenc Balint-Benczedi; Thiemo Wiedemeyer; Michael Beetz; Narunas Vaskevicius; Christian A. Mueller; Tobias Fromm; Andreas Birk
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Autonomous robots in unstructured and dynamically changing retail environments have to master complex perception, knowledgeprocessing, and manipulation tasks. To enable them to act competently, we propose a framework based on three core components: (o) a knowledge-enabled perception system, capable of combining diverse information sources to cope with occlusions and stacked objects with a variety of textures and shapes, (o) knowledge processing methods produce strategies for tidying up...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1605.04177
2
2.0
Jun 29, 2018
06/18
by
Oliver Kroemer; Gaurav S. Sukhatme
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Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1605.04439
2
2.0
Jun 29, 2018
06/18
by
Eyal En Gad; Akshay Gadde; A. Salman Avestimehr; Antonio Ortega
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This paper studies graph-based active learning, where the goal is to reconstruct a binary signal defined on the nodes of a weighted graph, by sampling it on a small subset of the nodes. A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph. The algorithm generalizes a recent method for sampling nodes in unweighted graphs. The generalization improves the sampling performance...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1605.05710
7
7.0
Jun 29, 2018
06/18
by
Prasenjit Karmakar; Rajkumar Maity; Shalabh Bhatnagar
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In this paper we provide a rigorous convergence analysis of a "off"-policy temporal difference learning algorithm with linear function approximation and per time-step linear computational complexity in "online" learning environment. The algorithm considered here is TDC with importance weighting introduced by Maei et al. We support our theoretical results by providing suitable empirical results for standard off-policy counterexamples.
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1605.06076
2
2.0
Jun 29, 2018
06/18
by
Yongxin Yang; Timothy Hospedales
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Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1605.06391
2
2.0
Jun 29, 2018
06/18
by
Chao Liu; Kin Gwn Lore; Soumik Sarkar
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Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, root-cause analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for root-cause analysis for addressing such issues. The framework is based on a...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1605.06421
5
5.0
Jun 29, 2018
06/18
by
Christian Walder
texts
eye 5
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In this paper, we consider the problem of probabilistically modelling symbolic music data. We introduce a representation which reduces polyphonic music to a univariate categorical sequence. In this way, we are able to apply state of the art natural language processing techniques, namely the long short-term memory sequence model. The representation we employ permits arbitrary rhythmic structure, which we assume to be given. We show that our model is effective on four out of four piano roll based...
Topics: Sound, Computing Research Repository
Source: http://arxiv.org/abs/1606.01368
4
4.0
Jun 29, 2018
06/18
by
Furong Huang
texts
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Unsupervised learning aims at the discovery of hidden structure that drives the observations in the real world. It is essential for success in modern machine learning. Latent variable models are versatile in unsupervised learning and have applications in almost every domain. Training latent variable models is challenging due to the non-convexity of the likelihood objective. An alternative method is based on the spectral decomposition of low order moment tensors. This versatile framework is...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1606.03212
3
3.0
Jun 29, 2018
06/18
by
Peter Buneman; Sławek Staworko
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We investigate the problem of aligning two RDF databases, an essential problem in understanding the evolution of ontologies. Our approaches address three fundamental challenges: 1) the use of "blank" (null) names, 2) ontology changes in which different names are used to identify the same entity, and 3) small changes in the data values as well as small changes in the graph structure of the RDF database. We propose approaches inspired by the classical notion of graph bisimulation and...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1606.08657
4
4.0
Jun 29, 2018
06/18
by
Francesco Riccio; Roberto Capobianco; Marc Hanheide; Daniele Nardi
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Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1607.00354
2
2.0
Jun 29, 2018
06/18
by
Parsa Bagherzadeh; Hadi Sadoghi Yazdi
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The presence of outliers is prevalent in machine learning applications and may produce misleading results. In this paper a new method for dealing with outliers and anomal samples is proposed. To overcome the outlier issue, the proposed method combines the global and local views of the samples. By combination of these views, our algorithm performs in a robust manner. The experimental results show the capabilities of the proposed method.
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1607.00466
3
3.0
Jun 29, 2018
06/18
by
Gaoying Ju; Yongkun Li; Yinlong Xu; Jiqiang Chen; John C. S. Lui
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In recent years, there is an increasing demand of big memory systems so to perform large scale data analytics. Since DRAM memories are expensive, some researchers are suggesting to use other memory systems such as non-volatile memory (NVM) technology to build large-memory computing systems. However, whether the NVM technology can be a viable alternative (either economically and technically) to DRAM remains an open question. To answer this question, it is important to consider how to design a...
Topics: Performance, Computing Research Repository
Source: http://arxiv.org/abs/1607.00714
3
3.0
Jun 29, 2018
06/18
by
Volodymyr Kuleshov; Stefano Ermon
texts
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Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems. Existing uncertainty estimation techniques may fail when their modeling assumptions are not met, e.g. when the data distribution differs from the one seen at training time. Here, we propose techniques that assess a classification algorithm's uncertainty via calibrated probabilities (i.e. probabilities that match empirical outcome frequencies in the long run) and which are...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1607.03594
2
2.0
Jun 29, 2018
06/18
by
Kai Herrmann; Hannes Voigt; Andreas Behrend; Jonas Rausch; Wolfgang Lehner
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We present InVerDa, a tool for end-to-end support of co-existing schema versions within one database. While it is state of the art to run multiple versions of a continuously developed application concurrently, the same is hard for databases. In order to keep multiple co-existing schema versions alive, that all access the same data set, developers usually employ handwritten delta code (e.g. views and triggers in SQL). This delta code is hard to write and hard to maintain: if a database...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1608.05564
2
2.0
Jun 29, 2018
06/18
by
Jaroslaw Szlichta; Parke Godfrey; Lukasz Golab; Mehdi Kargar; Divesh Srivastava
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Integrity constraints (ICs) provide a valuable tool for expressing and enforcing application semantics. However, formulating constraints manually requires domain expertise, is prone to human errors, and may be excessively time consuming, especially on large datasets. Hence, proposals for automatic discovery have been made for some classes of ICs, such as functional dependencies (FDs), and recently, order dependencies (ODs). ODs properly subsume FDs, as they can additionally express business...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1608.06169
3
3.0
Jun 29, 2018
06/18
by
Katsumi Kumai; Yuhki Shiraishi; Jianwei Zhang; Hiroyuki Kitagawa; Atsuyuki Morishima
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A common workflow to perform a continuous human task stream is to divide workers into groups, have one group perform the newly-arrived task, and rotate the groups. We call this type of workflow the group rotation. This paper addresses the problem of how to manage Group Rotation Type Crowdsourcing, the group rotation in a crowdsourcing setting. In the group-rotation type crowdsourcing, we must change the group structure dynamically because workers come in and leave frequently. This paper...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1609.00117
2
2.0
Jun 29, 2018
06/18
by
Jonas Schneider
texts
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Efficient evaluation of multi-dimensional range queries in a main-memory database is an important, but difficult task. State-of-the-art techniques rely on optimised sequential scans or tree-based structures. For range queries with small result sets, sequential scans exhibit poor asymptotic performance. Also, as the dimensionality of the data set increases, the performance of tree-based structures degenerates due to the curse of dimensionality. Recent literature proposed the Elf, a main-memory...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1609.01319
2
2.0
Jun 29, 2018
06/18
by
Weixiang Shao; Lifang He; Chun-Ta Lu; Xiaokai Wei; Philip S. Yu
texts
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In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1609.08286
3
3.0
Jun 29, 2018
06/18
by
Yonghui Xiao; Yilin Shen; Jinfei Liu; Li Xiong; Hongxia Jin; Xiaofeng Xu
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Hidden Markov model (HMM) has been well studied and extensively used. In this paper, we present DPHMM ({Differentially Private Hidden Markov Model}), an HMM embedded with a private data release mechanism, in which the privacy of the data is protected through a graph. Specifically, we treat every state in Markov model as a node, and use a graph to represent the privacy policy, in which "indistinguishability" between states is denoted by edges between nodes. Due to the temporal...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1609.09172
3
3.0
Jun 29, 2018
06/18
by
Waqqas Ahmad
texts
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This research investigates decentralized control of mobile robots specifically for coverage problems. There are different approaches associated with decentralized control strategy for coverage control problems. We perform a comparative review of these approaches and use the approach based on simple local coordination rules. We investigate this extensively used nearest neighbour rule based approach for developing coverage control algorithms. In this approach, a mobile robot gives an equal...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1609.09463
5
5.0
Jun 29, 2018
06/18
by
Luca Carlone; Sertac Karaman
texts
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Visual attention is the cognitive process that allows humans to parse a large amount of sensory data by selecting relevant information and filtering out irrelevant stimuli. This papers develops a computational framework for visual attention in robots. We consider a Visual Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor. The robot can allocate limited resources to VIN, due to time and energy constraints. Therefore, we...
Topics: Computing Research Repository, Robotics
Source: http://arxiv.org/abs/1610.03344
2
2.0
Jun 29, 2018
06/18
by
Weiran Wang; Xinchen Yan; Honglak Lee; Karen Livescu
texts
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We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks. We derive variational lower bounds of the data likelihood by parameterizing the posterior probability of the latent variables from the view that is available at test time. We also propose a variant of VCCA called VCCA-private that can, in addition to the...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1610.03454
3
3.0
Jun 29, 2018
06/18
by
Daniel Hein; Alexander Hentschel; Volkmar Sterzing; Michel Tokic; Steffen Udluft
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A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced. The Industrial Benchmark aims at being be realistic in the sense, that it includes a variety of aspects that we found to be vital in industrial applications. It is not designed to be an approximation of any real system, but to pose the same hardness and complexity.
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1610.03793
4
4.0
Jun 29, 2018
06/18
by
Manjesh Hanawal; Csaba Szepesvari; Venkatesh Saligrama
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In many security and healthcare systems a sequence of features/sensors/tests are used for detection and diagnosis. Each test outputs a prediction of the latent state, and carries with it inherent costs. Our objective is to {\it learn} strategies for selecting tests to optimize accuracy \& costs. Unfortunately it is often impossible to acquire in-situ ground truth annotations and we are left with the problem of unsupervised sensor selection (USS). We pose USS as a version of stochastic...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1610.05394
5
5.0
Jun 30, 2018
06/18
by
Luc Le Magoarou; Rémi Gribonval
texts
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Dictionary learning is a branch of signal processing and machine learning that aims at finding a frame (called dictionary) in which some training data admits a sparse representation. The sparser the representation, the better the dictionary. The resulting dictionary is in general a dense matrix, and its manipulation can be computationally costly both at the learning stage and later in the usage of this dictionary, for tasks such as sparse coding. Dictionary learning is thus limited to...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1406.5388
3
3.0
Jun 30, 2018
06/18
by
Yoshua Bengio
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We propose to exploit {\em reconstruction} as a layer-local training signal for deep learning. Reconstructions can be propagated in a form of target propagation playing a role similar to back-propagation but helping to reduce the reliance on derivatives in order to perform credit assignment across many levels of possibly strong non-linearities (which is difficult for back-propagation). A regularized auto-encoder tends produce a reconstruction that is a more likely version of its input, i.e., a...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1407.7906
2
2.0
Jun 30, 2018
06/18
by
Harry Halpin; James Cheney
texts
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While the Semantic Web currently can exhibit provenance information by using the W3C PROV standards, there is a "missing link" in connecting PROV to storing and querying for dynamic changes to RDF graphs using SPARQL. Solving this problem would be required for such clear use-cases as the creation of version control systems for RDF. While some provenance models and annotation techniques for storing and querying provenance data originally developed with databases or workflows in mind...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1408.0926
3
3.0
Jun 30, 2018
06/18
by
Zoltan Nagy; Csaba Nemes; Antal Hiba; Arpad Csik; Andras Kiss; Miklos Ruszinko; Peter Szolgay
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Accurate simulations of various physical processes on digital computers requires huge computing performance, therefore accelerating these scientific and engineering applications has a great importance. Density of programmable logic devices doubles in every 18 months according to Moore's Law. On the recent devices around one hundred double precision floating-point adders and multipliers can be implemented. In the paper an FPGA based framework is described to efficiently utilize this huge...
Topics: Performance, Computing Research Repository
Source: http://arxiv.org/abs/1408.5715
2
2.0
Jun 30, 2018
06/18
by
Xin Geng
texts
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Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named \emph{label distribution learning} (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1408.6027
3
3.0
Jun 30, 2018
06/18
by
Neel Shah; Vladimir Kolmogorov; Christoph H. Lampert
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Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut. In this work, we introduce a new algorithm for SSVM training that is more efficient than...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1408.6804
4
4.0
Jun 30, 2018
06/18
by
Jian Fang; Shaobo Lin; Zongben Xu
texts
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We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning. OSGA is concerned with selecting more than one atoms in each iteration step, which, of course, greatly reduces the computational burden when compared with the conventional orthogonal greedy algorithm (OGA). We prove that even for function classes that are not the convex hull of the dictionary, OSGA does not degrade the approximation capability of OGA provided the...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1409.5330
3
3.0
Jun 30, 2018
06/18
by
Marta Soare; Alessandro Lazaric; Rémi Munos
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We study the best-arm identification problem in linear bandit, where the rewards of the arms depend linearly on an unknown parameter $\theta^*$ and the objective is to return the arm with the largest reward. We characterize the complexity of the problem and introduce sample allocation strategies that pull arms to identify the best arm with a fixed confidence, while minimizing the sample budget. In particular, we show the importance of exploiting the global linear structure to improve the...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1409.6110
3
3.0
Jun 30, 2018
06/18
by
Dalia Attia Waguih; Laure Berti-Equille
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A fundamental problem in data fusion is to determine the veracity of multi-source data in order to resolve conflicts. While previous work in truth discovery has proved to be useful in practice for specific settings, sources' behavior or data set characteristics, there has been limited systematic comparison of the competing methods in terms of efficiency, usability, and repeatability. We remedy this deficit by providing a comprehensive review of 12 state-of-the art algorithms for truth...
Topics: Databases, Computing Research Repository
Source: http://arxiv.org/abs/1409.6428
2
2.0
Jun 30, 2018
06/18
by
Apoorv Agarwal; Anna Choromanska; Krzysztof Choromanski
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In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k needs to be set by the user of the algorithms. (Kang 2013) recently proposed a novel use of determinantal point processes for sampling the initial centroids for the KMEANS algorithm (we call it KMEANSD++). They, however, do not provide any evaluation establishing that...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1410.6975
2
2.0
Jun 30, 2018
06/18
by
Mariusz Bojarski; Anna Choromanska; Krzysztof Choromanski; Yann LeCun
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We consider supervised learning with random decision trees, where the tree construction is completely random. The method is popularly used and works well in practice despite the simplicity of the setting, but its statistical mechanism is not yet well-understood. In this paper we provide strong theoretical guarantees regarding learning with random decision trees. We analyze and compare three different variants of the algorithm that have minimal memory requirements: majority voting, threshold...
Topics: Computing Research Repository, Learning
Source: http://arxiv.org/abs/1410.6973
