42 research outputs found
Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains
No full textNational Institutes of Health http://dx.doi.org/10.13039/100000002National Science Foundation http://dx.doi.org/10.13039/100000001Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661Medical Research FoundationNational Institute for Health Research http://dx.doi.org/10.13039/501100000272National Institute for Health Research http://dx.doi.org/10.13039/501100000272National Institute for Health Research http://dx.doi.org/10.13039/501100000272Medical Research Council http://dx.doi.org/10.13039/501100000265Sixth Framework Programme http://dx.doi.org/10.13039/100011103Human Brain ProjectMedical Research Council http://dx.doi.org/10.13039/50110000026
Advances in Deep Learning through Gradient Amplification and Applications
No full textDeep neural networks currently play a prominent role in solving problems across a wide variety of disciplines. Improving performance of deep learning models and reducing their training times are some of the ongoing challenges. Increasing the depth of the networks improves performance but suffers from the problem of vanishing gradients and increased training times. In this research, we design methods to address these challenges in deep neural networks and demonstrate deep learning applications in several domains. We propose a gradient amplification based approach to train deep neural networks, which improves their training and testing accuraries, addresses vanishing gradients, as well as reduces the training time by reaching higher accuracies even at higher learning rates. We also develop an integrated training strategy to enable/disable amplification at certain epochs. Detailed analysis is performed on different neural networks using random amplification, where the layers to be amplified are selected randomly. The implications of gradient amplification on the number of layers, types of layers, amplification factors, training strategies and learning rates are studied in detail. With this knowledge, effective ways to update gradients are designed to perform amplification at layer-level and also at neuron-level. Lastly, we provide applications of deep learning methods to some of the challenging problems in the areas of smartgrids and bioinformatics. Deep neural networks with feed forward architectures are used to solve data integrity attacks in smart grids. We propose an image based preprocessing method to convert heterogenous genomic sequences into images which are then classified to detect Hepatitis C virus(HCV) infection stages. In summary, this research advances deep learning techniques and their applications to real world problems
Sunitha Krishnan i jej walka z seksualnym niewolnictwem kobiet w Indiach
No full textThis article introduces the silhouette of Hindu activist Sunitha Krishan, has been strongly committed to the fight against sexual slavery in India for 20 years. It presents the circumstances of the fact that she started her business, initiatives which was undertaken by her and what was the results of her activities. In summary form the article presents stories that Krishnan has collected by seeing with victims – women and children. The author also essays the presentation of Hindu models of femininity and she demonstrates their influence on the current situation of women. This text is largery based on online sources, articles published on the information portals, interviews with Krishnan and record during the conference with her participation.Niniejszy artykuł przybliża sylwetkę hinduskiej aktywistki Sunithy Krishan, która od 20 lat jest silnie zaangażowana w walkę z seksualnym niewolnictwem w Indiach. Przedstawia okoliczności, w jakich doszło do rozpoczęcia jej działalności, inicjatywy przez nią podejmowane oraz rezultaty działań. W skrótowej formie prezentuje opowieści, które zebrała Krishnan spotykając się z pokrzywdzonymi kobietami i dziećmi. Autorka podejmuje również próbę przedstawienia hinduskich modeli kobiecości i wykazania jaki mają one wpływ na obecną sytuację kobiet. Tekst oparty jest w dużej mierze na źródłach internetowych, artykułach publikowanych na portalach informacyjnych, wywiadach przeprowadzonych z Krishnan oraz zapisie konferencji z jej udziałem.Udostępnienie publikacji Wydawnictwa Uniwersytetu Łódzkiego finansowane w ramach projektu „Doskonałość naukowa kluczem do doskonałości kształcenia”. Projekt realizowany jest ze środków Europejskiego Funduszu Społecznego w ramach Programu Operacyjnego Wiedza Edukacja Rozwój; nr umowy: POWER.03.05.00-00-Z092/17-00
Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge
No full textDeep Learning for Asphyxiated Infant Cry Classification Based on Acoustic Features and Weighted Prosodic Features
No full textA Survey on Algorithms for Intelligent Computing and Smart City Applications
No full textWith the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed
Multi‐view learning for benign epilepsy with centrotemporal spikes
No full textBenign epilepsy with centrotemporal spikes (BECT) may be the most popular epilepsy to attack children. In recent years, more and more studies have shown that magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) are promising techniques in distinguishing BECT patients from healthy controls. However, these existing works have suffered from two limitations. On the one hand, they have paid more attention to the brain changes between BETC and healthy controls than developing machine learning methods that can recognize BECT patients. On the other hand, most of the existing approaches extract hand‐crafted features from MRI or fMRI, which cannot obtain the desired performance due to the limited representative capacity of the used features. To address these issues, we propose a novel classification method by fusing the predictions of three different views: hand‐crafted features view, MRI view, and fMRI view. The final result is obtained by passing through those predictions after a fusing neural network. The basic idea of our method is that multiple views could provide complementary information and thus can boost the classification performance. Extensive experiments show that the proposed multi‐view method is remarkably superior to single‐view methods
Intelligent gradient amplification for deep neural networks
No full textDeep learning models offer superior performance compared to other machine
learning techniques for a variety of tasks and domains, but pose their own
challenges. In particular, deep learning models require larger training times
as the depth of a model increases, and suffer from vanishing gradients. Several
solutions address these problems independently, but there have been minimal
efforts to identify an integrated solution that improves the performance of a
model by addressing vanishing gradients, as well as accelerates the training
process to achieve higher performance at larger learning rates. In this work,
we intelligently determine which layers of a deep learning model to apply
gradient amplification to, using a formulated approach that analyzes gradient
fluctuations of layers during training. Detailed experiments are performed for
simpler and deeper neural networks using two different intelligent measures and
two different thresholds that determine the amplification layers, and a
training strategy where gradients are amplified only during certain epochs.
Results show that our amplification offers better performance compared to the
original models, and achieves accuracy improvement of around 2.5% on CIFAR- 10
and around 4.5% on CIFAR-100 datasets, even when the models are trained with
higher learning rates
Antioxidative properties of fermented poultry green bones
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
