9,748 research outputs found
The group-based social skills training SOSTA-FRA in children and adolescents with high functioning autism spectrum disorder - study protocol of the randomised, multi-centre controlled SOSTA - net trial
Background: Group-based social skills training (SST) has repeatedly been recommended as treatment of choice in high-functioning autism spectrum disorder (HFASD). To date, no sufficiently powered randomised controlled trial has been performed to establish efficacy and safety of SST in children and adolescents with HFASD. In this randomised, multi-centre, controlled trial with 220 children and adolescents with HFASD it is hypothesized, that add-on group-based SST using the 12 weeks manualised SOSTAâFRA program will result in improved social responsiveness (measured by the parent rated social responsiveness scale, SRS) compared to treatment as usual (TAU). It is further expected, that parent and self reported anxiety and depressive symptoms will decline and pro-social behaviour will increase in the treatment group. A neurophysiological study in the Frankfurt HFASD subgroup will be performed pre- and post treatment to assess changes in neural function induced by SST versus TAU.
Methods/design: The SOSTA â net trial is designed as a prospective, randomised, multi-centre, controlled trial with two parallel groups. The primary outcome is change in SRS score directly after the intervention and at 3 months follow-up. Several secondary outcome measures are also obtained. The target sample consists of 220 individuals with ASD, included at the six study centres.
Discussion: This study is currently one of the largest trials on SST in children and adolescents with HFASD worldwide. Compared to recent randomised controlled studies, our study shows several advantages with regard to in- and exclusion criteria, study methods, and the therapeutic approach chosen, which can be easily implemented in non-university-based clinical settings.
Trial registration: ISRCTN94863788 â SOSTA â net: Group-based social skills training in children and adolescents with high functioning autism spectrum disorder
Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy
Sketch-a-Net that Beats Humans
We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral
Neural Network Ensembles for Time Series Prediction
Rapidly evolving businesses generate massive
amounts of time-stamped data sequences and defy a demand
for massively multivariate time series analysis. For such data
the predictive engine shifts from the historical auto-regression
to modelling complex non-linear relationships between multidimensional
features and the time series outputs. In order to
exploit these time-disparate relationships for the improved time
series forecasting, the system requires a flexible methodology
of combining multiple prediction models applied to multiple
versions of the temporal data under significant noise component
and variable temporal depth of predictions. In reply
to this challenge a composite time series prediction model
is proposed which combines the strength of multiple neural
network (NN) regressors applied to the temporally varied
feature subsets and the postprocessing smoothing of outputs
developed to further reduce noise. The key strength of the model
is its excellent adaptability and generalisation ability achieved
through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006
and NN3 Competition 2007 concerning prediction of univariate
and multivariate time-series. It showed the best predictive
performance among 12 competitive models in the NISIS 2006
and is under evaluation within NN3 2007 Competition
Reinforcement Learning using Augmented Neural Networks
Neural networks allow Q-learning reinforcement learning agents such as deep
Q-networks (DQN) to approximate complex mappings from state spaces to value
functions. However, this also brings drawbacks when compared to other function
approximators such as tile coding or their generalisations, radial basis
functions (RBF) because they introduce instability due to the side effect of
globalised updates present in neural networks. This instability does not even
vanish in neural networks that do not have any hidden layers. In this paper, we
show that simple modifications to the structure of the neural network can
improve stability of DQN learning when a multi-layer perceptron is used for
function approximation.Comment: 7 pages; two columns; 4 figure
Improving Language Modelling with Noise-contrastive estimation
Neural language models do not scale well when the vocabulary is large.
Noise-contrastive estimation (NCE) is a sampling-based method that allows for
fast learning with large vocabularies. Although NCE has shown promising
performance in neural machine translation, it was considered to be an
unsuccessful approach for language modelling. A sufficient investigation of the
hyperparameters in the NCE-based neural language models was also missing. In
this paper, we showed that NCE can be a successful approach in neural language
modelling when the hyperparameters of a neural network are tuned appropriately.
We introduced the 'search-then-converge' learning rate schedule for NCE and
designed a heuristic that specifies how to use this schedule. The impact of the
other important hyperparameters, such as the dropout rate and the weight
initialisation range, was also demonstrated. We showed that appropriate tuning
of NCE-based neural language models outperforms the state-of-the-art
single-model methods on a popular benchmark
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