7,974 research outputs found
Phonological Priming In Young Children Who Stutter: Holistic Versus Incremental Processing
Purpose: To investigate the holistic versus incremental phonological encoding processes of young children who stutter (CWS; N = 26) and age- and gender-matched children who do not stutter (CWNS; N = 26) via a picture-naming auditory priming paradigm. Method: Children named pictures during 3 auditory priming conditions: neutral, holistic, and incremental. Speech reaction time (SRT) was measured from the onset of picture presentation to the onset of participant response. Results: CWNS shifted from being significantly faster in the holistic priming condition to being significantly faster in the incremental priming condition from 3 to 5 years of age. In contrast, the majority of 3- and 5-year-old CWS continued to exhibit faster SRT in the holistic than the incremental condition. Conclusion: CWS are delayed in making the developmental shift in phonological encoding from holistic to incremental processing, a delay that may contribute to their difficulties establishing fluent speech.Communication Sciences and Disorder
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
Nonparametric causal effects based on incremental propensity score interventions
Most work in causal inference considers deterministic interventions that set
each unit's treatment to some fixed value. However, under positivity violations
these interventions can lead to non-identification, inefficiency, and effects
with little practical relevance. Further, corresponding effects in longitudinal
studies are highly sensitive to the curse of dimensionality, resulting in
widespread use of unrealistic parametric models. We propose a novel solution to
these problems: incremental interventions that shift propensity score values
rather than set treatments to fixed values. Incremental interventions have
several crucial advantages. First, they avoid positivity assumptions entirely.
Second, they require no parametric assumptions and yet still admit a simple
characterization of longitudinal effects, independent of the number of
timepoints. For example, they allow longitudinal effects to be visualized with
a single curve instead of lists of coefficients. After characterizing these
incremental interventions and giving identifying conditions for corresponding
effects, we also develop general efficiency theory, propose efficient
nonparametric estimators that can attain fast convergence rates even when
incorporating flexible machine learning, and propose a bootstrap-based
confidence band and simultaneous test of no treatment effect. Finally we
explore finite-sample performance via simulation, and apply the methods to
study time-varying sociological effects of incarceration on entry into
marriage
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