Machines learning techniques plays a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as autoML. In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.
To the best of our knowledge, the only approach that uses a non-predetermined sequence of operators is @cite_36 , but it is not possible to add additional constraints.
@cite_36
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5 @math 3.5 ) for a previously published four class card pip recognition task and an accuracy of 84.9 @math 1.9 for a new more difficult 36 class character recognition task. Describing a video sequence in terms of a small number of coherently moving segments is useful for tasks ranging from video compression to event perception. A promising approach is to view the motion segmentation problem in a mixture estimation framework. However, existing formulations generally use only the motion, data and thus fail to make use of static cues when segmenting the sequence. Furthermore, the number of models is either specified in advance or estimated outside the mixture model framework. In this work we address both of these issues. We show how to add spatial constraints to the mixture formulations and present a variant of the EM algorithm that males use of both the form and the motion constraints. Moreover this algorithm estimates the number of segments given knowledge about the level of model failure expected in the sequence. The algorithm's performance is illustrated on synthetic and real image sequences. The goal of Complex Event Processing (CEP) systems is to efficiently detect complex patterns over a stream of primitive events. A pattern of particular significance is a sequence, where we are interested in identifying that a number of primitive events have arrived on the stream in a predefined order. Many popular CEP systems employ Non-deterministic Finite Automata (NFA) arranged in a chain topology to detect such sequences. Existing NFA-based mechanisms incrementally extend previously observed prefixes of a sequence until a match is found. Consequently, each newly arriving event needs to be processed to determine whether a new prefix is to be initiated or an existing one extended. This approach may be very inefficient when events at the beginning of the sequence are very frequent. We address the problem by introducing a lazy evaluation mechanism that is able to process events in descending order of selectivity. We employ this mechanism in a chain topology NFA, which waits until the most selective event in the sequence arrives and then adds events to partial matches according to a predetermined order of selectivity. In addition, we propose a tree topology NFA that does not require the selectivity order to be defined in advance. Finally, we experimentally evaluate our mechanism on real-world stock trading data, demonstrating a performance gain of two orders of magnitude, with significantly reduced memory resource requirements.