17,685 research outputs found

    Why is unsupervised alignment of English embeddings from different algorithms so hard?

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    This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms. Why is that? We believe understanding why, is key to understand both modern word embedding algorithms and the limitations and instability dynamics of GANs. This paper shows that (a) in all these cases, where alignment fails, there exists a linear transform between the two embeddings (so algorithm biases do not lead to non-linear differences), and (b) similar effects can not easily be obtained by varying hyper-parameters. One plausible suggestion based on our initial experiments is that the differences in the inductive biases of the embedding algorithms lead to an optimization landscape that is riddled with local optima, leading to a very small basin of convergence, but we present this more as a challenge paper than a technical contribution.Comment: Accepted at EMNLP 201

    Optimisation of policies for transport integration in metropolitan areas: report on work packages 30 and 40

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    The overall objectives of Project OPTIMA are:- (i) to identify optimal urban transport strategies for a range of urban areas within the EU; (ii) to compare the strategies which are specified as optimal in different cities, and to assess the reasons for these differences; (iii) to assess the acceptability and feasibility of implementation of these strategies both in the case study cities and more widely in the EU, and (iv) to use the results to provide more general guidance on urban transport policy within the EU. There is a wide range of objectives of transport policy in urban areas, but most can be grouped under the broad headings of economic efficiency, including economic development, on the one hand, and sustainability, including environment, safety, equity and quality of life, on the other. It is now generally accepted that the overall strategy for achieving these objectives must include an element of reduction of private car use and transfer of travel to other modes. The policy instruments for achieving these objectives can include infrastructure provision, management measures to enhance other modes and to restrict car use, and pricing measures to make public transport more attractive and to increase the marginal cost of car use. It is now widely accepted that the most appropriate strategy will involve several of these measures, combined in an integrated way which emphasises the synergy between them. The most appropriate strategy for a city will depend on its size, the current built form, topography, transport infrastructure and patterns of use; levels of car ownership, congestion and projected growth in travel; transport policy instruments already in use; and the acceptability of other measures in political and legislative terms. These will differ from city to city. Policy advice cannot therefore be generalised, but must be developed for a range of different types of city. This is the approach adopted in this study, in which nine different cities in five countries (Edinburgh, Merseyside, Vienna, Eisenstadt, Trams@, Oslo, Helsinki, Torino and Salerno) have been studied in detail, using a common study methodology. This report summarises the output of two work packages in OPTIMA: WP30: Test Combinations of Policy Instruments WP40: Identify Optim

    Learning Petri net models of non-linear gene interactions

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    Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or “explanation” of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene–gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene–gene interactions recently reported in the literature

    A Minimal Architecture for General Cognition

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    A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to transfer architectural inspiration from biology into artificial intelligence, MANIC seeks to minimize novelty and follow the most well-established constructs that have evolved within various sub-fields of data science. From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence. This paper provides a theoretical analysis of the MANIC architecture.Comment: 8 pages, 8 figures, conference, Proceedings of the 2015 International Joint Conference on Neural Network

    Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

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    We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state- of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.Comment: Paper presented at CVPR 1

    Social learning strategies modify the effect of network structure on group performance

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    The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines

    Conservation of high-flux backbone in alternate optimal and near-optimal flux distributions of metabolic networks

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    Constraint-based flux balance analysis (FBA) has proven successful in predicting the flux distribution of metabolic networks in diverse environmental conditions. FBA finds one of the alternate optimal solutions that maximizes the biomass production rate. Almaas et al have shown that the flux distribution follows a power law, and it is possible to associate with most metabolites two reactions which maximally produce and consume a give metabolite, respectively. This observation led to the concept of high-flux backbone (HFB) in metabolic networks. In previous work, the HFB has been computed using a particular optima obtained using FBA. In this paper, we investigate the conservation of HFB of a particular solution for a given medium across different alternate optima and near-optima in metabolic networks of E. coli and S. cerevisiae. Using flux variability analysis (FVA), we propose a method to determine reactions that are guaranteed to be in HFB regardless of alternate solutions. We find that the HFB of a particular optima is largely conserved across alternate optima in E. coli, while it is only moderately conserved in S. cerevisiae. However, the HFB of a particular near-optima shows a large variation across alternate near-optima in both organisms. We show that the conserved set of reactions in HFB across alternate near-optima has a large overlap with essential reactions and reactions which are both uniquely consuming (UC) and uniquely producing (UP). Our findings suggest that the structure of the metabolic network admits a high degree of redundancy and plasticity in near-optimal flow patterns enhancing system robustness for a given environmental condition.Comment: 11 pages, 6 figures, to appear in Systems and Synthetic Biolog

    Money Creation in a Random Matching Model

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    I study money creation in versions of the Trejos-Wright (1995) and Shi (1995) models with indivisible money and individual holdings bounded at two units. I work with the same class of policies as in Deviatov and Wallace (2001), who study money creation in that model. However, I consider an alternative notion of implementability–the ex ante pairwise core. I compute a set of numerical examples to determine whether money creation is beneficial. I find beneficial e?ects of money creation if individuals are su?ciently risk averse (obtain su?ciently high utility gains from trade) and impatient.inflation; Friedman rule; optimal monetary policy

    Coordinated optimization of visual cortical maps : 2. Numerical studies

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    In the juvenile brain, the synaptic architecture of the visual cortex remains in a state of flux for months after the natural onset of vision and the initial emergence of feature selectivity in visual cortical neurons. It is an attractive hypothesis that visual cortical architecture is shaped during this extended period of juvenile plasticity by the coordinated optimization of multiple visual cortical maps such as orientation preference (OP), ocular dominance (OD), spatial frequency, or direction preference. In part (I) of this study we introduced a class of analytically tractable coordinated optimization models and solved representative examples, in which a spatially complex organization of the OP map is induced by interactions between the maps. We found that these solutions near symmetry breaking threshold predict a highly ordered map layout. Here we examine the time course of the convergence towards attractor states and optima of these models. In particular, we determine the timescales on which map optimization takes place and how these timescales can be compared to those of visual cortical development and plasticity. We also assess whether our models exhibit biologically more realistic, spatially irregular solutions at a finite distance from threshold, when the spatial periodicities of the two maps are detuned and when considering more than 2 feature dimensions. We show that, although maps typically undergo substantial rearrangement, no other solutions than pinwheel crystals and stripes dominate in the emerging layouts. Pinwheel crystallization takes place on a rather short timescale and can also occur for detuned wavelengths of different maps. Our numerical results thus support the view that neither minimal energy states nor intermediate transient states of our coordinated optimization models successfully explain the architecture of the visual cortex. We discuss several alternative scenarios that may improve the agreement between model solutions and biological observations
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