10,514 research outputs found
Conditional Activation for Diverse Neurons in Heterogeneous Networks
In this paper, we propose a new scheme for modelling the diverse behavior of
neurons. We introduce the conditional activation, in which a neurons activation
function is dynamically modified by a control signal. We apply this method to
recreate behavior of special neurons existing in the human auditory and visual
system. A heterogeneous multilayered perceptron (MLP) incorporating the
developed models demonstrates simultaneous improvement in learning speed and
performance across a various number of hidden units and layers, compared to a
homogeneous network composed of the conventional neuron model. For similar
performance, the proposed model lowers the memory for storing network
parameters significantly
Deep Learning for Cross-Technology Communication Design
Recently, it was shown that a communication system could be represented as a
deep learning (DL) autoencoder. Inspired by this idea, we target the problem of
OFDM-based wireless cross-technology communication (CTC) where both
in-technology and CTC transmissions take place simultaneously. We propose
DeepCTC, a DL-based autoencoder approach allowing us to exploit DL for joint
optimization of transmitter and receivers for both in-technology as well as CTC
communication in an end-to-end manner. Different from classical CTC designs, we
can easily weight in-technology against CTC communication. Moreover, CTC
broadcasts can be efficiently realized even in the presence of heterogeneous
CTC receivers with diverse OFDM technologies. Our numerical analysis confirms
the feasibility of DeepCTC as both in-technology and CTC messages can be
decoded with sufficient low block error rate.Comment: 6 pages, 8 figure
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
In this work, we present the Text Conditioned Auxiliary Classifier Generative
Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network
(GAN) for synthesizing images from their text descriptions. Former approaches
have tried to condition the generative process on the textual data; but allying
it to the usage of class information, known to diversify the generated samples
and improve their structural coherence, has not been explored. We trained the
presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the
discriminability of the generated images with Inception-Score, as well as their
diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our
approach outperforms the state-of-the-art models, i.e., its inception score is
3.45, corresponding to a relative increase of 7.8% compared to the recently
introduced StackGan. A comparison of the mean MS-SSIM scores of the training
and generated samples per class shows that our approach is able to generate
highly diverse images with an average MS-SSIM of 0.14 over all generated
classes
Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli
Sensory stimuli can be recognized more rapidly when they are expected. This
phenomenon depends on expectation affecting the cortical processing of sensory
information. However, virtually nothing is known on the mechanisms responsible
for the effects of expectation on sensory networks. Here, we report a novel
computational mechanism underlying the expectation-dependent acceleration of
coding observed in the gustatory cortex (GC) of alert rats. We use a recurrent
spiking network model with a clustered architecture capturing essential
features of cortical activity, including the metastable activity observed in GC
before and after gustatory stimulation. Relying both on network theory and
computer simulations, we propose that expectation exerts its function by
modulating the intrinsically generated dynamics preceding taste delivery. Our
model, whose predictions are confirmed in the experimental data, demonstrates
how the modulation of intrinsic metastable activity can shape sensory coding
and mediate cognitive processes such as the expectation of relevant events.
Altogether, these results provide a biologically plausible theory of
expectation and ascribe a new functional role to intrinsically generated,
metastable activity.Comment: 37 pages, 4+3 figures; v2: improved results, 7 new supplementary
figures; refs adde
Treatment of Semantic Heterogeneity in Information Retrieval
The first step to handle semantic heterogeneity should be the attempt to
enrich the semantic information about documents, i.e. to fill up the gaps in
the documents meta-data automatically. Section 2 describes a set of cascading
deductive and heuristic extraction rules, which were developed in the project
CARMEN for the domain of Social Sciences. The mapping between different
terminologies can be done by using intellectual, statistical and/or neural
network transfer modules. Intellectual transfers use cross-concordances between
different classification schemes or thesauri. Section 3 describes the creation,
storage and handling of such transfers.Comment: Technical Report (Arbeitsbericht) GESIS - Leibniz Institute for the
Social Science
Particle Identification In Camera Image Sensors Using Computer Vision
We present a deep learning, computer vision algorithm constructed for the
purposes of identifying and classifying charged particles in camera image
sensors. We apply our algorithm to data collected by the Distributed Electronic
Cosmic-ray Observatory (DECO), a global network of smartphones that monitors
camera image sensors for the signatures of cosmic rays and other energetic
particles, such as those produced by radioactive decays. The algorithm, whose
core component is a convolutional neural network, achieves classification
performance comparable to human quality across four distinct DECO event
topologies. We apply our model to the entire DECO data set and determine a
selection that achieves purity for all event types. In particular, we
estimate a purity of when applied to cosmic-ray muons. The automated
classification is run on the public DECO data set in real time in order to
provide classified particle interaction images to users of the app and other
interested members of the public.Comment: 14 pages, 14 figures, 1 tabl
Deep Learning: A Bayesian Perspective
Deep learning is a form of machine learning for nonlinear high dimensional
pattern matching and prediction. By taking a Bayesian probabilistic
perspective, we provide a number of insights into more efficient algorithms for
optimisation and hyper-parameter tuning. Traditional high-dimensional data
reduction techniques, such as principal component analysis (PCA), partial least
squares (PLS), reduced rank regression (RRR), projection pursuit regression
(PPR) are all shown to be shallow learners. Their deep learning counterparts
exploit multiple deep layers of data reduction which provide predictive
performance gains. Stochastic gradient descent (SGD) training optimisation and
Dropout (DO) regularization provide estimation and variable selection. Bayesian
regularization is central to finding weights and connections in networks to
optimize the predictive bias-variance trade-off. To illustrate our methodology,
we provide an analysis of international bookings on Airbnb. Finally, we
conclude with directions for future research
Group Emotion Recognition Using Machine Learning
Automatic facial emotion recognition is a challenging task that has gained
significant scientific interest over the past few years, but the problem of
emotion recognition for a group of people has been less extensively studied.
However, it is slowly gaining popularity due to the massive amount of data
available on social networking sites containing images of groups of people
participating in various social events. Group emotion recognition is a
challenging problem due to obstructions like head and body pose variations,
occlusions, variable lighting conditions, variance of actors, varied indoor and
outdoor settings and image quality. The objective of this task is to classify a
group's perceived emotion as Positive, Neutral or Negative. In this report, we
describe our solution which is a hybrid machine learning system that
incorporates deep neural networks and Bayesian classifiers. Deep Convolutional
Neural Networks (CNNs) work from bottom to top, analysing facial expressions
expressed by individual faces extracted from the image. The Bayesian network
works from top to bottom, inferring the global emotion for the image, by
integrating the visual features of the contents of the image obtained through a
scene descriptor. In the final pipeline, the group emotion category predicted
by an ensemble of CNNs in the bottom-up module is passed as input to the
Bayesian Network in the top-down module and an overall prediction for the image
is obtained. Experimental results show that the stated system achieves 65.27%
accuracy on the validation set which is in line with state-of-the-art results.
As an outcome of this project, a Progressive Web Application and an
accompanying Android app with a simple and intuitive user interface are
presented, allowing users to test out the system with their own pictures
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Scenario generation is an important step in the operation and planning of
power systems with high renewable penetrations. In this work, we proposed a
data-driven approach for scenario generation using generative adversarial
networks, which is based on two interconnected deep neural networks. Compared
with existing methods based on probabilistic models that are often hard to
scale or sample from, our method is data-driven, and captures renewable energy
production patterns in both temporal and spatial dimensions for a large number
of correlated resources. For validation, we use wind and solar times-series
data from NREL integration data sets. We demonstrate that the proposed method
is able to generate realistic wind and photovoltaic power profiles with full
diversity of behaviors. We also illustrate how to generate scenarios based on
different conditions of interest by using labeled data during training. For
example, scenarios can be conditioned on weather events~(e.g. high wind day) or
time of the year~(e,g. solar generation for a day in July). Because of the
feedforward nature of the neural networks, scenarios can be generated extremely
efficiently without sophisticated sampling techniques.Comment: Accepted to IEEE Transactions on Power Systems; code available at
https://github.com/chennnnnyize/Renewables_Scenario_Gen_GA
Deep Convolutional Decision Jungle for Image Classification
We propose a novel method called deep convolutional decision jungle (CDJ) and
its learning algorithm for image classification. The CDJ maintains the
structure of standard convolutional neural networks (CNNs), i.e. multiple
layers of multiple response maps fully connected. Each response map-or node-in
both the convolutional and fully-connected layers selectively respond to class
labels s.t. each data sample travels via a specific soft route of those
activated nodes. The proposed method CDJ automatically learns features, whereas
decision forests and jungles require pre-defined feature sets. Compared to
CNNs, the method embeds the benefits of using data-dependent discriminative
functions, which better handles multi-modal/heterogeneous data; further,the
method offers more diverse sparse network responses, which in turn can be used
for cost-effective learning/classification. The network is learnt by combining
conventional softmax and proposed entropy losses in each layer. The entropy
loss,as used in decision tree growing, measures the purity of data activation
according to the class label distribution. The back-propagation rule for the
proposed loss function is derived from stochastic gradient descent (SGD)
optimization of CNNs. We show that our proposed method outperforms
state-of-the-art methods on three public image classification benchmarks and
one face verification dataset. We also demonstrate the use of auxiliary data
labels, when available, which helps our method to learn more discriminative
routing and representations and leads to improved classification
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