450 research outputs found

    A Multi-Channel Neural Graphical Event Model with Negative Evidence

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    Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.Comment: AAAI 202

    Combining Sources of Preference Data: The Case of the Lurking l‘s

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    This paper brings together several research streams and concepts that have been evolving in random utility choice theory: first, it reviews the literature on stated preference (SP) elicitation methods and introduces the concept of testing data generation process invariance across SP and revealed preference (RP) choice data sources; second, it proposes a general data generation process an useful framework for viewing this data combination process; third, it describes the evolution of discrete choice models within the random utility family, where progressively more behavioural realism is being achieved by relaxing strong assumptions on the role of the variance structure (specifically heteroscedasticity) of the unobserved effects. This latter topic is central to the issue of combining multiple data sources. Particular choice model formulations incorporating heteroscedastic effects are presented, discussed and applied to data. The rich insights possible from modeling heteroscedasticity in choice processes is illustrated in each of the empirical applications, which examine its relevance to issues of data combination and taste heterogeneity

    Analyzing Human-Human Interactions: A Survey

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    Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording setting, the appearance of the people depicted and the coordinated performance of their interaction. This survey provides a summary of these challenges and datasets to address these, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on deep learning and convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art to analyze and, eventually, understand social human actions

    Feature predictability flexibly supports auditory stream segregation or integration

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    Many sound sources emit series of discrete sounds. Auditory perception must bind these sounds together (stream integration) while separating them from sounds emitted by other sources (stream segregation). One cue for identifying successive sounds that belong together is the predictability between their feature values. Previous studies have demonstrated that independent predictable patterns appearing separately in two interleaved sound sequences support perceptual segregation. The converse case, whether a joint predictable pattern in a mixture of interleaved sequences supports perceptual integration, has not yet been put to a rigorous empirical test. This was mainly due to difficulties in manipulating the predictability of the full sequence independently of the predictability of the interleaved subsequences. The present study implemented such an independent manipulation. Listeners continuously indicated whether they perceived a tone sequence as integrated or segregated, while predictable patterns set up to support one or the other percept were manipulated without the participants’ knowledge. Perceptual reports demonstrate that predictability supports stream segregation or integration depending on the type of predictable pattern that is present in the sequence. The effects of predictability were so pronounced as to qualitatively flip perception from predominantly (62%) integrated to predominantly (73%) segregated. These results suggest that auditory perception flexibly responds to encountered regular patterns, favoring predictable perceptual organizations over unpredictable ones. Besides underlining the role of predictability as a cue within auditory scene analysis, the present design also provides a general framework that accommodates previous investigations focusing on sub-comparisons within the present set of experimental manipulations. Results of intermediate conditions shed light on why some previous studies have obtained little to no effects of predictability on auditory scene analysis

    Rhythmic complexity and predictive coding::A novel approach to modeling rhythm and meter perception in music

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    Musical rhythm, consisting of apparently abstract intervals of accented temporal events, has a remarkable capacity to move our minds and bodies. How does the cognitive system enable our experiences of rhythmically complex music? In this paper, we describe some common forms of rhythmic complexity in music and propose the theory of predictive coding (PC) as a framework for understanding how rhythm and rhythmic complexity are processed in the brain. We also consider why we feel so compelled by rhythmic tension in music. First, we consider theories of rhythm and meter perception, which provide hierarchical and computational approaches to modeling. Second, we present the theory of PC, which posits a hierarchical organization of brain responses reflecting fundamental, survival-related mechanisms associated with predicting future events. According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Third, we develop a PC model of musical rhythm, in which rhythm perception is conceptualized as an interaction between what is heard (“rhythm”) and the brain’s anticipatory structuring of music (“meter”). Finally, we review empirical studies of the neural and behavioral effects of syncopation, polyrhythm and groove, and propose how these studies can be seen as special cases of the PC theory. We argue that musical rhythm exploits the brain’s general principles of prediction and propose that pleasure and desire for sensorimotor synchronization from musical rhythm may be a result of such mechanisms

    Conflicting Objectives in Decisions

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    This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem
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