Spelling suggestions: "subject:"deep aeural anetwork"" "subject:"deep aeural enetwork""
41 |
Identifikace osob pomocí hlubokých neuronových sítí / Deep Neural Networks for Person IdentificationDuban, Michal January 2016 (has links)
This master's thesis deals with design and implementation of convolutional neural networks used in person re-identification. Implemented convolutional neural networks were tested on two datasets CUHK01 a CUHK03. Results, comparable with state of the art methods were acheved on these datasets. Designed networks were implemented in Caffe framework.
|
42 |
Applications of Deep Neural Networks in Computer-Aided Drug DesignAhmadreza Ghanbarpour Ghouchani (10137641) 01 March 2021 (has links)
<div>Deep neural networks (DNNs) have gained tremendous attention over the recent years due to their outstanding performance in solving many problems in different fields of science and technology. Currently, this field is of interest to many researchers and growing rapidly. The ability of DNNs to learn new concepts with minimal instructions facilitates applying current DNN-based methods to new problems. Here in this dissertation, three methods based on DNNs are discussed, tackling different problems in the field of computer-aided drug design.</div><div><br></div><div>The first method described addresses the problem of prediction of hydration properties from 3D structures of proteins without requiring molecular dynamics simulations. Water plays a major role in protein-ligand interactions and identifying (de)solvation contributions of water molecules can assist drug design. Two different model architectures are presented for the prediction the hydration information of proteins. The performance of the methods are compared with other conventional methods and experimental data. In addition, their applications in ligand optimization and pose prediction is shown.</div><div><br></div><div>The design of de novo molecules has always been of interest in the field of drug discovery. The second method describes a generative model that learns to derive features from protein sequences to design de novo compounds. We show how the model can be used to generate molecules similar to the known for the targets the model have not seen before and compare with benchmark generative models.</div><div><br></div><div>Finally, it is demonstrated how DNNs can learn to predict secondary structure propensity values derived from NMR ensembles. Secondary structure propensities are important in identifying flexible regions in proteins. Protein flexibility has a major role in drug-protein binding, and identifying such regions can assist in development of methods for ligand binding prediction. The prediction performance of the method is shown for several proteins with two or more known secondary structure conformations.</div>
|
43 |
Speech to Text for Swedish using KALDI / Tal till text, utvecklandet av en svensk taligenkänningsmodell i KALDIKullmann, Emelie January 2016 (has links)
The field of speech recognition has during the last decade left the re- search stage and found its way in to the public market. Most computers and mobile phones sold today support dictation and transcription in a number of chosen languages. Swedish is often not one of them. In this thesis, which is executed on behalf of the Swedish Radio, an Automatic Speech Recognition model for Swedish is trained and the performance evaluated. The model is built using the open source toolkit Kaldi. Two approaches of training the acoustic part of the model is investigated. Firstly, using Hidden Markov Model and Gaussian Mixture Models and secondly, using Hidden Markov Models and Deep Neural Networks. The later approach using deep neural networks is found to achieve a better performance in terms of Word Error Rate. / De senaste åren har olika tillämpningar inom människa-dator interaktion och främst taligenkänning hittat sig ut på den allmänna marknaden. Många system och tekniska produkter stöder idag tjänsterna att transkribera tal och diktera text. Detta gäller dock främst de större språken och sällan finns samma stöd för mindre språk som exempelvis svenskan. I detta examensprojekt har en modell för taligenkänning på svenska ut- vecklas. Det är genomfört på uppdrag av Sveriges Radio som skulle ha stor nytta av en fungerande taligenkänningsmodell på svenska. Modellen är utvecklad i ramverket Kaldi. Två tillvägagångssätt för den akustiska träningen av modellen är implementerade och prestandan för dessa två är evaluerade och jämförda. Först tränas en modell med användningen av Hidden Markov Models och Gaussian Mixture Models och slutligen en modell där Hidden Markov Models och Deep Neural Networks an- vänds, det visar sig att den senare uppnår ett bättre resultat i form av måttet Word Error Rate.
|
44 |
Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation modelsvon Koch, Christian, Anzén, William January 2021 (has links)
Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision. There are many prominent convolutional neural network architectures in the literature, and this thesis explores two: a modified U-Net and the PSPNet. The architectures were combined with three loss functions and three class weighting schemes resulting in 18 model configurations that were evaluated and compared. This thesis also explores transfer learning techniques for neural networks tasked with identifying slag in images from inside a furnace. The benefit of transfer learning is that the network can learn to find features from already labeled data of another context. Finally, the thesis explored how temporal information could be utilised by adding an LSTM layer to a model taking pairs of images as input, instead of one. The results show (1) that the PSPNet outperformed the U-Net for all tested configurations in all relevant metrics, (2) that the model is able to find more complex features while converging quicker by using transfer learning, and (3) that utilising temporal information reduced the variance of the predictions, and that the modified PSPNet using an LSTM layer showed promise in handling images with outlying characteristics.
|
45 |
A Comparative Study of Reinforcement-based and Semi-classical Learning in Sensor FusionBodén, Johan January 2021 (has links)
Reinforcement learning has proven itself very useful in certain areas, such as games. However, the approach has been seen as quite limited. Reinforcement-based learning has for instance not been commonly used for classification tasks as it is receiving feedback on how well it did for an action performed on a specific input. This slows the performance convergence rate as compared to other classification approaches which has the input and the corresponding output to train on. Nevertheless, this thesis aims to investigate whether reinforcement-based learning could successfully be employed on a classification task. Moreover, as sensor fusion is an expanding field which can for instance assist autonomous vehicles in understanding its surroundings, it is also interesting to see how sensor fusion, i.e., fusion between lidar and RGB images, could increase the performance in a classification task. In this thesis, a reinforcement-based learning approach is compared to a semi-classical approach. As an example of a reinforcement learning model, a deep Q-learning network was chosen, and a support vector machine classifier built on top of a deep neural network, was chosen as an example of a semi-classical model. In this work, these frameworks are compared with and without sensor fusion to see whether fusion improves their performance. Experiments show that the evaluated reinforcement-based learning approach underperforms in terms of metrics but mainly due to its slow learning process, in comparison to the semi-classical approach. However, on the other hand using reinforcement-based learning to carry out a classification task could still in some cases be advantageous, as it still performs fairly well in terms of the metrics presented in this work, e.g. F1-score, or for instance imbalanced datasets. As for the impact of sensor fusion, a notable improvement can be seen, e.g. when training the deep Q-learning model for 50 episodes, the F1-score increased with 0.1329; especially, when taking into account that the most of the lidar data used in the fusion is lost since this work projects the 3D lidar data onto the same 2D plane as the RGB images.
|
46 |
Design Space Exploration and Architecture Design for Inference and Training Deep Neural NetworksQi, Yangjie January 2021 (has links)
No description available.
|
47 |
Efficient Adaptation of Deep Vision ModelsZe Wang (15354715) 27 April 2023 (has links)
<p>Deep neural networks have made significant advances in computer vision. However, several challenges limit their real-world applications. For example, domain shifts in vision data degrade model performance; visual appearance variances affect model robustness; it is also non-trivial to extend a model trained on one task to novel tasks; and in many applications, large-scale labeled data are not even available for learning powerful deep models from scratch. This research focuses on improving the transferability of deep features and the efficiency of deep vision model adaptation, leading to enhanced generalization and new capabilities on computer vision tasks. Specifically, we approach these problems from the following two directions: architectural adaptation and label-efficient transferable feature learning. From an architectural perspective, we investigate various schemes that permit network adaptation to be parametrized by multiple copies of sub-structures, distributions of parameter subspaces, or functions that infer parameters from data. We also explore how model adaptation can bring new capabilities, such as continuous and stochastic image modeling, fast transfer to new tasks, and dynamic computation allocation based on sample complexity. From the perspective of feature learning, we show how transferable features emerge from generative modeling with massive unlabeled or weakly labeled data. Such features enable both image generation under complex conditions and downstream applications like image recognition and segmentation. By combining both perspectives, we achieve improved performance on computer vision tasks with limited labeled data, enhanced transferability of deep features, and novel capabilities beyond standard deep learning models.</p>
|
48 |
Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug DiscoveryBrown, Benjamin P., Vu, Oanh, Geanes, Alexander R., Kothiwale, Sandeepkumar, Butkiewicz, Mariusz, Lowe Jr., Edward W., Mueller, Ralf, Pape, Richard, Mendenhall, Jeffrey, Meiler, Jens 04 April 2023 (has links)
The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic
open-source software package designed to integrate traditional small molecule
cheminformatics tools with machine learning-based quantitative structure-activity/
property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a
detailed introduction to core BCL cheminformatics functionality, showing how traditional
tasks (e.g., computing chemical properties, estimating druglikeness) can be readily
combined with machine learning. In addition, we have included multiple examples
covering areas of advanced use, such as reaction-based library design. We anticipate
that this manuscript will be a valuable resource for researchers in computer-aided drug
discovery looking to integrate modular cheminformatics and machine learning tools into
their pipelines.
|
49 |
Prediction of securities' behavior using a multi-level artificial neural network with extra inputs between layers / Förutsägelse av värdepapperens beteende med hjälp av ett artificiellt neuralt nätverk med flera nivåer med extra ingångar mellan skiktenTörnqvist, Eric, Guan, Xing January 2017 (has links)
This paper discusses the possibilities of predicting changes in stock pricing at a high frequency applying a multi-level neural network without the use of recurrent neurons or any other time series analysis, as suggested in a paper byChen et al. [2017]. The paper tries to adapt the model presented in a paper by Chen et al. [2017] by making the network deeper, feeding it data of higher resolution and changing the activation functions. While the resulting accuracy is not as high as other models, this paper might prove useful for those interested in further developing neural networks using data with high resolution and to the fintech business as a whole.
|
50 |
Privacy leaks from deep linear networks : Information leak via shared gradients in federated learning systems / Sekretessläckor från djupa linjära nätverk : Informationsläckor via delning av gradienter i distribuerade lärande systemShi, Guangze January 2022 (has links)
The field of Artificial Intelligence (AI) has always faced two major challenges. The first is that data is kept scattered and cannot be collected for more efficiently use. The second is that data privacy and security need to be continuously strengthened. Based on these two points, federated learning is proposed as an emerging machine learning scheme. The idea of federated learning is to collaboratively train neural networks on servers. Each user receives the current weights of the network and then sequentially sends parameter updates (gradients) based on their own data. Because the input data remains on-device and only the parameter gradients are shared, this scheme is considered to be effective in preserving data privacy. Some previous attacks also provide a false sense of security since they only succeed in contrived settings, even for a single image. Our research mainly focus on attacks on shared gradients, showing experimentally that private training data can be obtained from publicly shared gradients. We do experiments on both linear-based and convolutional-based deep networks, whose results show that our attack is capable of creating a threat to data privacy, and this threat is independent of the specific structure of neural networks. The method presented in this paper is only to illustrate that it is feasible to recover user data from shared gradients, and cannot be used as an attack to obtain privacy in large quantities. The goal is to spark further research on federated learning, especially gradient security. We also make some brief discussion on possible strategies against our attack methods of privacy. Different methods have their own advantages and disadvantages in terms of privacy protection. Therefore, data pre-processing and network structure adjustment may need to be further researched, so that the process of training the models can achieve better privacy protection while maintaining high precision. / Området artificiell intelligens har alltid stått inför två stora utmaningar. Den första är att data hålls utspridda och inte kan samlas in för mer effektiv användning. Det andra är att datasekretess och säkerhet behöver stärkas kontinuerligt. Baserat på dessa två punkter föreslås federerat lärande som ett framväxande angreppssätt inom maskininlärning. Tanken med federerat lärande är att tillsammans träna neurala nätverk på servrar. Varje användare får nätverkets aktuella vikter och skickar sedan parameteruppdateringar (gradienter) sekventiellt baserat på sina egna data. Eftersom indata förblir på enheten och endast parametergradienterna delas, anses detta schema vara effektivt för att bevara datasekretessen. Vissa tidigare attacker ger också en falsk känsla av säkerhet eftersom de bara lyckas i konstruerade inställningar, även för en enda bild. Vår forskning fokuserar främst på attacker på delade gradienter, och visar experimentellt att privat träningsdata kan erhållas från offentligt delade gradienter. Vi gör experiment på både linjärbaserade och faltningsbaserade djupa nätverk, vars resultat visar att vår attack kan skapa ett hot mot dataintegriteten, och detta hot är oberoende av den specifika strukturen hos djupa nätverk. Metoden som presenteras i denna rapport är endast för att illustrera att det är möjligt att rekonstruera användardata från delade gradienter, och kan inte användas som en attack för att erhålla integritet i stora mängder. Målet är att få igång ytterligare forskning om federerat lärande, särskilt gradientsäkerhet. Vi gör också en kort diskussion om möjliga strategier mot våra attackmetoder för integritet. Olika metoder har sina egna fördelar och nackdelar när det gäller integritetsskydd. Därför kan förbearbetning av data och justering av nätverksstruktur behöva undersökas ytterligare, så att processen med att träna modellerna kan uppnå bättre integritetsskydd samtidigt som hög precision bibehålls.
|
Page generated in 0.0426 seconds