6,766 research outputs found
On the determination of probability density functions by using Neural Networks
It is well known that the output of a Neural Network trained to disentangle
between two classes has a probabilistic interpretation in terms of the
a-posteriori Bayesian probability, provided that a unary representation is
taken for the output patterns. This fact is used to make Neural Networks
approximate probability density functions from examples in an unbinned way,
giving a better performace than ``standard binned procedures''. In addition,
the mapped p.d.f. has an analytical expression.Comment: 13 pages including 3 eps figures. Submitted to Comput. Phys. Commu
A comparative study of the D0 neural-network analysis of the top quark non-leptonic decay channel
A simpler neural-network approach is presented for the analysis of the top
quark non-leptonic decay channel in events of the D0 Collaboration. Results for
the top quark signal are comparable to those found by the D0 Collaboration by a
more elaborate handling of the event information used as input to the neural
network.Comment: 5 pages, 1 figur
Analyzing the Performance of Multilayer Neural Networks for Object Recognition
In the last two years, convolutional neural networks (CNNs) have achieved an
impressive suite of results on standard recognition datasets and tasks.
CNN-based features seem poised to quickly replace engineered representations,
such as SIFT and HOG. However, compared to SIFT and HOG, we understand much
less about the nature of the features learned by large CNNs. In this paper, we
experimentally probe several aspects of CNN feature learning in an attempt to
help practitioners gain useful, evidence-backed intuitions about how to apply
CNNs to computer vision problems.Comment: Published in European Conference on Computer Vision 2014 (ECCV-2014
Learning Dilation Factors for Semantic Segmentation of Street Scenes
Contextual information is crucial for semantic segmentation. However, finding
the optimal trade-off between keeping desired fine details and at the same time
providing sufficiently large receptive fields is non trivial. This is even more
so, when objects or classes present in an image significantly vary in size.
Dilated convolutions have proven valuable for semantic segmentation, because
they allow to increase the size of the receptive field without sacrificing
image resolution. However, in current state-of-the-art methods, dilation
parameters are hand-tuned and fixed. In this paper, we present an approach for
learning dilation parameters adaptively per channel, consistently improving
semantic segmentation results on street-scene datasets like Cityscapes and
Camvid.Comment: GCPR201
A novel approach to error function minimization for feedforward neural networks
Feedforward neural networks with error backpropagation (FFBP) are widely
applied to pattern recognition. One general problem encountered with this type
of neural networks is the uncertainty, whether the minimization procedure has
converged to a global minimum of the cost function. To overcome this problem a
novel approach to minimize the error function is presented. It allows to
monitor the approach to the global minimum and as an outcome several
ambiguities related to the choice of free parameters of the minimization
procedure are removed.Comment: 11 pages, latex, 3 figures appended as uuencoded fil
Real-time localised forecasting of the Madden-Julian Oscillation using neural network models
Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higher-order MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea
Application of Neural Networks for Energy Reconstruction
The possibility to use Neural Networks for reconstruction of the energy
deposited in the calorimetry system of the CMS detector is investigated. It is
shown that using feed - forward neural network, good linearity, Gaussian energy
distribution and good energy resolution can be achieved. Significant
improvement of the energy resolution and linearity is reached in comparison
with other weighting methods for energy reconstruction.Comment: 18 pages, 13 figures, LATEX, submitted to: Nuclear Instruments &
Methods
Contractive De-noising Auto-encoder
Auto-encoder is a special kind of neural network based on reconstruction.
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to
the input by corrupting the original data first and then reconstructing the
original input by minimizing the reconstruction error function. And contractive
auto-encoder (CAE) is another kind of improved auto-encoder to learn robust
feature by introducing the Frobenius norm of the Jacobean matrix of the learned
feature with respect to the original input. In this paper, we combine
de-noising auto-encoder and contractive auto- encoder, and propose another
improved auto-encoder, contractive de-noising auto- encoder (CDAE), which is
robust to both the original input and the learned feature. We stack CDAE to
extract more abstract features and apply SVM for classification. The experiment
result on benchmark dataset MNIST shows that our proposed CDAE performed better
than both DAE and CAE, proving the effective of our method.Comment: Figures edite
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
We propose a weakly-supervised framework for action labeling in video, where
only the order of occurring actions is required during training time. The key
challenge is that the per-frame alignments between the input (video) and label
(action) sequences are unknown during training. We address this by introducing
the Extended Connectionist Temporal Classification (ECTC) framework to
efficiently evaluate all possible alignments via dynamic programming and
explicitly enforce their consistency with frame-to-frame visual similarities.
This protects the model from distractions of visually inconsistent or
degenerated alignments without the need of temporal supervision. We further
extend our framework to the semi-supervised case when a few frames are sparsely
annotated in a video. With less than 1% of labeled frames per video, our method
is able to outperform existing semi-supervised approaches and achieve
comparable performance to that of fully supervised approaches.Comment: To appear in ECCV 201
Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed
Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio
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