114,751 research outputs found

    On the definition of a general learning system with user-defined operators

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    In this paper, we push forward the idea of machine learning systems whose operators can be modified and fine-tuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators affect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is defined as a choice of operator and rule. As a result, the architecture can be seen as a 'system for writing machine learning systems' or to explore new operators where the policy reuse (as a kind of transfer learning) is allowed. States and actions are represented in a Q matrix which is actually a table, from which a supervised model is learnt. This makes it possible to have a more flexible mapping between old and new problems, since we work with an abstraction of rules and actions. We include some examples sharing reuse and the application of the system gErl to IQ problems. In order to evaluate gErl, we will test it against some structured problems: a selection of IQ test tasks and some experiments on some structured prediction problems (list patterns)

    A New Automatic Method to Adjust Parameters for Object Recognition

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    To recognize an object in an image, the user must apply a combination of operators, where each operator has a set of parameters. These parameters must be well adjusted in order to reach good results. Usually, this adjustment is made manually by the user. In this paper we propose a new method to automate the process of parameter adjustment for an object recognition task. Our method is based on reinforcement learning, we use two types of agents: User Agent that gives the necessary information and Parameter Agent that adjusts the parameters of each operator. Due to the nature of reinforcement learning the results do not depend only on the system characteristics but also on the user favorite choices

    A Multi-Agents Architecture to Learn Vision Operators and their Parameters

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    In a vision system, every task needs that the operators to apply should be {\guillemotleft} well chosen {\guillemotright} and their parameters should be also {\guillemotleft} well adjusted {\guillemotright}. The diversity of operators and the multitude of their parameters constitute a big challenge for users. As it is very difficult to make the {\guillemotleft} right {\guillemotright} choice, lack of a specific rule, many disadvantages appear and affect the computation time and especially the quality of results. In this paper we present a multi-agent architecture to learn the best operators to apply and their best parameters for a class of images. Our architecture consists of three types of agents: User Agent, Operator Agent and Parameter Agent. The User Agent determines the phases of treatment, a library of operators and the possible values of their parameters. The Operator Agent constructs all possible combinations of operators and the Parameter Agent, the core of the architecture, adjusts the parameters of each combination by treating a large number of images. Through the reinforcement learning mechanism, our architecture does not consider only the system opportunities but also the user preferences.Comment: IJCSI, May 201

    Declarative Data Analytics: a Survey

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    The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.Comment: 36 pages, 2 figure

    Interactive Program Synthesis

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    Program synthesis from incomplete specifications (e.g. input-output examples) has gained popularity and found real-world applications, primarily due to its ease-of-use. Since this technology is often used in an interactive setting, efficiency and correctness are often the key user expectations from a system based on such technologies. Ensuring efficiency is challenging since the highly combinatorial nature of program synthesis algorithms does not fit in a 1-2 second response expectation of a user-facing system. Meeting correctness expectations is also difficult, given that the specifications provided are incomplete, and that the users of such systems are typically non-programmers. In this paper, we describe how interactivity can be leveraged to develop efficient synthesis algorithms, as well as to decrease the cognitive burden that a user endures trying to ensure that the system produces the desired program. We build a formal model of user interaction along three dimensions: incremental algorithm, step-based problem formulation, and feedback-based intent refinement. We then illustrate the effectiveness of each of these forms of interactivity with respect to synthesis performance and correctness on a set of real-world case studies

    A Tensor Based Data Model for Polystore: An Application to Social Networks Data

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    In this article, we show how the mathematical object tensor can be used to build a multi-paradigm model for the storage of social data in data warehouses. From an architectural point of view, our approach allows to link different storage systems (polystore) and limits the impact of ETL tools performing model transformations required to feed different analysis algorithms. Therefore, systems can take advantage of multiple data models both in terms of query execution performance and the semantic expressiveness of data representation. The proposed model allows to reach the logical independence between data and programs implementing analysis algorithms. With a concrete case study on message virality on Twitter during the French presidential election of 2017, we highlight some of the contributions of our model

    Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties

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    We present a general framework to learn functions in tensor product reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a novel representer theorem suitable for existing as well as new spectral penalties for tensors. When the functions in the TP-RKHS are defined on the Cartesian product of finite discrete sets, in particular, our main problem formulation admits as a special case existing tensor completion problems. Other special cases include transfer learning with multimodal side information and multilinear multitask learning. For the latter case, our kernel-based view is instrumental to derive nonlinear extensions of existing model classes. We give a novel algorithm and show in experiments the usefulness of the proposed extensions

    Learning to Match for Multi-criteria Document Relevance

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    In light of the tremendous amount of data produced by social media, a large body of research have revisited the relevance estimation of the users' generated content. Most of the studies have stressed the multidimensional nature of relevance and proved the effectiveness of combining the different criteria that it embodies. Traditional relevance estimates combination methods are often based on linear combination schemes. However, despite being effective, those aggregation mechanisms are not effective in real-life applications since they heavily rely on the non-realistic independence property of the relevance dimensions. In this paper, we propose to tackle this issue through the design of a novel fuzzy-based document ranking model. We also propose an automated methodology to capture the importance of relevance dimensions, as well as information about their interaction. This model, based on the Choquet Integral, allows to optimize the aggregated documents relevance scores using any target information retrieval relevance metric. Experiments within the TREC Microblog task and a social personalized information retrieval task highlighted that our model significantly outperforms a wide range of state-of-the-art aggregation operators, as well as a representative learning to rank methods.Comment: 9 page

    Designing a GUI for Proofs - Evaluation of an HCI Experiment

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    Often user interfaces of theorem proving systems focus on assisting particularly trained and skilled users, i.e., proof experts. As a result, the systems are difficult to use for non-expert users. This paper describes a paper and pencil HCI experiment, in which (non-expert) students were asked to make suggestions for a GUI for an interactive system for mathematical proofs. They had to explain the usage of the GUI by applying it to construct a proof sketch for a given theorem. The evaluation of the experiment provides insights for the interaction design for non-expert users and the needs and wants of this user group

    Helix: Holistic Optimization for Accelerating Iterative Machine Learning

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    Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Unfortunately, existing machine learning systems focus narrowly on model training---a small fraction of the overall development time---and neglect to address iterative development. We propose Helix, a machine learning system that optimizes the execution across iterations---intelligently caching and reusing, or recomputing intermediates as appropriate. Helix captures a wide variety of application needs within its Scala DSL, with succinct syntax defining unified processes for data preprocessing, model specification, and learning. We demonstrate that the reuse problem can be cast as a Max-Flow problem, while the caching problem is NP-Hard. We develop effective lightweight heuristics for the latter. Empirical evaluation shows that Helix is not only able to handle a wide variety of use cases in one unified workflow but also much faster, providing run time reductions of up to 19x over state-of-the-art systems, such as DeepDive or KeystoneML, on four real-world applications in natural language processing, computer vision, social and natural sciences
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