Dissertationen zum Thema „Generative sequence models“
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Svensk, Gustav. „TDNet : A Generative Model for Taxi Demand Prediction“. Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.
Der volle Inhalt der QuelleGoodman, Genghis. „A Machine Learning Approach to Artificial Floorplan Generation“. UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.
Der volle Inhalt der QuelleTubiana, Jérôme. „Restricted Boltzmann machines : from compositional representations to protein sequence analysis“. Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE039/document.
Der volle Inhalt der QuelleRestricted Boltzmann machines (RBM) are graphical models that learn jointly a probability distribution and a representation of data. Despite their simple architecture, they can learn very well complex data distributions such the handwritten digits data base MNIST. Moreover, they are empirically known to learn compositional representations of data, i.e. representations that effectively decompose configurations into their constitutive parts. However, not all variants of RBM perform equally well, and little theoretical arguments exist for these empirical observations. In the first part of this thesis, we ask how come such a simple model can learn such complex probability distributions and representations. By analyzing an ensemble of RBM with random weights using the replica method, we have characterised a compositional regime for RBM, and shown under which conditions (statistics of weights, choice of transfer function) it can and cannot arise. Both qualitative and quantitative predictions obtained with our theoretical analysis are in agreement with observations from RBM trained on real data. In a second part, we present an application of RBM to protein sequence analysis and design. Owe to their large size, it is very difficult to run physical simulations of proteins, and to predict their structure and function. It is however possible to infer information about a protein structure from the way its sequence varies across organisms. For instance, Boltzmann Machines can leverage correlations of mutations to predict spatial proximity of the sequence amino-acids. Here, we have shown on several synthetic and real protein families that provided a compositional regime is enforced, RBM can go beyond structure and extract extended motifs of coevolving amino-acids that reflect phylogenic, structural and functional constraints within proteins. Moreover, RBM can be used to design new protein sequences with putative functional properties by recombining these motifs at will. Lastly, we have designed new training algorithms and model parametrizations that significantly improve RBM generative performance, to the point where it can compete with state-of-the-art generative models such as Generative Adversarial Networks or Variational Autoencoders on medium-scale data
Rehn, Martin. „Aspects of memory and representation in cortical computation“. Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4161.
Der volle Inhalt der QuelleIn this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level -- and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.
QC 20100916
Shimagaki, Kai. „Advanced statistical modeling and variable selection for protein sequences“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS548.
Der volle Inhalt der QuelleOver the last few decades, protein sequencing techniques have been developed and continuous experiments have been done. Thanks to all of these efforts, nowadays, we have obtained more than two hundred million protein sequence data. In order to deal with such a huge amount of biological data, now, we need theories and technologies to extract information that we can understand and interpret.The key idea to resolve this problem is statistical physics and the state of the art of machine learning (ML). Statistical physics is a field of physics that can successfully describe many complex systems by extracting or reducing variables to be interpretable variables based on simple principles. ML, on the other hand, can represent data (such as reconstruction and classification) without assuming how the data was generated, i.e. physical phenomenon behind of data. In this dissertation, we report studies of protein sequence generative modeling and protein-residue contact predictions using statistical physics-inspired modeling and ML-oriented methods. In the first part, we review the general background of biology and genomics. Then we discuss statistical modelings for protein sequence. In particular, we review Direct Coupling Analysis (DCA), which is the core technology of our research. We also discuss the effects of higher-order statistics contained in protein sequences and introduces deep learning-based generative models as a model that can go beyond pairwise interaction
Adak, Bulent Mehmet. „Model-based Code Generation For The High Level Architecture Federates“. Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12609032/index.pdf.
Der volle Inhalt der Quelles behavior model. The behavior model is based on Live Sequence Charts (LSCs), adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). The FAMM is constructed conforming to metaGME, the meta-metamodel offered by Generic Modeling Environment (GME). FAMM serves as a formal language for describing federation architectures. We present a code generator that generates Java/AspectJ code directly from a federation architecture model. An objective is to help verify a federation architecture by testing it early in the development lifecycle. Another objective is to help developers construct complete federate applications. Our approach to achieve these objectives is aspect-oriented in that the code generated from the LSC in conjunction with the Federation Object Model (FOM) serves as the base code on which the computation logic is weaved as an aspect.
Kunst, Rafael. „Um injetor de erros aplicado à avaliação de desempenho do codificador de canal em redes IEEE 802.16“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17800.
Der volle Inhalt der QuelleThe demand for providing multimedia services is increasing the development of wireless networks. Therefore, an important issue is to guarantee correct transmissions over channels that are affected by time and frequency variant conditions caused by physical impairments that lead to the occurrence of errors during the transmission. These errors are basically of two types: burst errors and random errors, typically modeled as Additive White Gaussian Noise (AWGN). Simulating the behavior of wireless channels affected by physical impairments has been subject of several investigations in the past years. Nevertheless, part of the current researches does not consider the occurrence of both errors at the same time. This approach may lead to imprecisions on the results obtained through simulations. This work proposea an error sequence generator which is able of generating both burst and AWGN error models. Moreover, the proposed model can generate hybrid errors sequences composed of both error types simultaneously. The proposed error sequence generator is applied to a case study that aims to evaluate the performance of the channel encoder of nomadic (fixed) and mobile IEEE 802.16 networks. In this context, we evaluate the error correction capability of FEC encoders which are mandatory according to IEEE 802.16 standard. Furthermore, we study the impact caused by the application of time diversity techniques on the transmission, considering scenarios affected by burst errors and AWGN. We also present a study about the theoretical throughput that can be reached by nomadic and mobile technologies. Finally, we discuss the technological advances brought by Orthogonal Frequency Division Multiple Access (OFDMA) channel multiplexing technique, which is employed in IEEE 802.16 mobile networks.
Künstner, Axel. „Birds as a Model for Comparative Genomic Studies“. Doctoral thesis, Uppsala universitet, Evolutionsbiologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-159766.
Der volle Inhalt der QuelleAlsafi, Radi Taha M. „Generation of complex recombinant fowlpox virus 9 (FP9) encoding simian immunodeficiency virus (SIVmac239) sequences as a model HIV vaccine candidate“. Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/generation-of-complex-recombinant-fowlpox-virus-9-fp9-encoding-simian-immunodeficiency-virus-sivmac239-sequences-as-a-model-hiv-vaccine-candidate(1a015762-8dc2-4153-a586-d7fab88b9658).html.
Der volle Inhalt der QuelleBlazejewski, Tomasz. „Generative Models for Synthetic Biology“. Thesis, 2020. https://doi.org/10.7916/d8-0xvy-cw79.
Der volle Inhalt der QuelleChiang, Yi-Heng, und 蔣宜衡. „Using the Sequence to Sequence Generative Model for Bidirectional Text Rewriting“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/trmcm5.
Der volle Inhalt der Quelle淡江大學
資訊管理學系碩士班
106
Although the ability to understand and master a language varies from person to person, it is also affected by the evolution of the language itself. In particular, Classical Chinese as a written language of the past has obvious differences from Vernacular Chinese used in modern society. As a consequence, many Chinese today find it hard to understand Classical Chinese texts. In order to bridge the gap in understanding the two writing styles of Classical Chinese and Vernacular Chinese, this work chooses the bidirectional text rewriting of Classical and Vernacular Chinese as the topic. A parallel corpus is collected and processed by natural language techniques. The corpus is used to train a sequence to sequence model under the deep learning architecture. The model can be used to generate sentences of the desired writing style. In addition, this work also uses two separate monolingual corpora to train two independent sets of word vectors in Classical Chinese and Vernacular Chinese, respectively. It aims to extract the semantic relevance between words in each writing style. From the parallel corpus, this work tries to find the correspondence relations between Classical Chinese (CC) and Vernacular Chinese (VC). A neural machine translation model is applied to extract the relevant word alignments in the parallel corpus. As result, the BLEU metric is used to evaluate the generated sentences. For the test dataset, it is found that the word-level model can rewrite VC to CC better than CC to VC. In contrast, the character-level model can rewrite CC to VC better than VC to CC. Overall, the character-level model performs better than the word-level model in Chinese text rewriting. In this work, natural language technologies are applied in rewriting between the two Chinese writing styles of Vernacular Chinese and Classical Chinese. It can be seen that the bidirectional text rewriting method used in this work has provided a promising study direction for understanding related writing styles.
Shen, Liang-Hsin, und 沈亮欣. „Acrostic Generating System: An Application of Control Signals on Sequence-to-Sequence Models“. Thesis, 2019. http://ndltd.ncl.edu.tw/handle/92fu45.
Der volle Inhalt der Quelle國立臺灣大學
資訊工程學研究所
107
An acrostic is a form of writing that the first token of each line (or other recurring features in the text) forms a meaningful sequence. In this paper we present a generalized acrostic generation system that can hide certain message in a flexible pattern specified by the users. Different from previous works that focus on rule-based solutions, this work adopts a neural-based sequence-to-sequence model to achieve this goal. Besides acrostic, users are also allowed to specify the rhyme and length of the output sequences. Based on our knowledge, this is the first neural-based natural language generation system that demonstrates the capability of performing micro-level control over output sentences.
Sridhar, Adepu. „Generating Test Sequences and Slices for Simulink/Stateflow Models“. Thesis, 2013. http://ethesis.nitrkl.ac.in/5000/1/211CS3301.pdf.
Der volle Inhalt der QuelleMontella, Sébastien, und 李胤龍. „Emotionally-Triggered Short Text Conversation using Attention-Based Sequence Generation Models“. Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hfpcxx.
Der volle Inhalt der Quelle國立中央大學
資訊工程學系
107
Emotional Intelligence is a field from which awareness is heavily being raised. Coupled with language generation, one expects to further humanize the machine and be a step closer to the user by generating responses that are consistent with a specific emotion. The analysis of sentiment within documents or sentences have been widely studied and improved while the generation of emotional content remains under-researched. Meanwhile, generative models have recently known series of improvements thanks to Generative Adversarial Network (GAN). Promising results are frequently reported in both natural language processing and computer vision. However, when applied to text generation, adversarial learning may lead to poor quality sentences and mode collapse. In this paper, we leverage one-round data conversation from social media to propose a novel approach in order to generate grammatically-correct-and-emotional-consistent answers for Short-Text Conversation task (STC-3) for NTCIR-14 workshop. We make use of an Attention-based Sequence-to-Sequence as our generator, inspired from StarGAN framework. We provide emotion embeddings and direct feedback from an emotion classifier to guide the generator. To avoid the aforementioned issues with adversarial networks, we alternatively train our generator using maximum likelihood and adversarial loss.
Felix, Reyes Alejandro. „Test case generation using symbolic grammars and quasirandom sequences“. Master's thesis, 2010. http://hdl.handle.net/10048/1668.
Der volle Inhalt der QuelleSoftware Engineering and Intelligent Systems
Xu, Kelvin. „Exploring Attention Based Model for Captioning Images“. Thèse, 2017. http://hdl.handle.net/1866/20194.
Der volle Inhalt der QuelleHao, Yangyang. „Computational modeling for identification of low-frequency single nucleotide variants“. 2015. http://hdl.handle.net/1805/8891.
Der volle Inhalt der QuelleReliable detection of low-frequency single nucleotide variants (SNVs) carries great significance in many applications. In cancer genetics, the frequencies of somatic variants from tumor biopsies tend to be low due to contamination with normal tissue and tumor heterogeneity. Circulating tumor DNA monitoring also faces the challenge of detecting low-frequency variants due to the small percentage of tumor DNA in blood. Moreover, in population genetics, although pooled sequencing is cost-effective compared with individual sequencing, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by multiple sources of errors, especially next-generation sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 5%; most fail to consider differential, context-specific sequencing artifacts. To face this challenge, we developed a computational and experimental framework, RareVar, to reliably identify low-frequency SNVs from high-throughput sequencing data. For optimized performance, RareVar utilized a supervised learning framework to model artifacts originated from different components of a specific sequencing pipeline. This is enabled by a customized, comprehensive benchmark data enriched with known low-frequency SNVs from the sequencing pipeline of interest. Genomic-context-specific sequencing error model was trained on the benchmark data to characterize the systematic sequencing artifacts, to derive the position-specific detection limit for sensitive low-frequency SNV detection. Further, a machine-learning algorithm utilized sequencing quality features to refine SNV candidates for higher specificity. RareVar outperformed existing approaches, especially at 0.5% to 5% frequency. We further explored the influence of statistical modeling on position specific error modeling and showed zero-inflated negative binomial as the best-performed statistical distribution. When replicating analyses on an Illumina MiSeq benchmark dataset, our method seamlessly adapted to technologies with different biochemistries. RareVar enables sensitive detection of low-frequency SNVs across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.
Andere, Anne A. „De novo genome assembly of the blow fly Phormia regina (Diptera: Calliphoridae)“. Thesis, 2014. http://hdl.handle.net/1805/5630.
Der volle Inhalt der QuellePhormia regina (Meigen), commonly known as the black blow fly is a dipteran that belongs to the family Calliphoridae. Calliphorids play an important role in various research fields including ecology, medical studies, veterinary and forensic sciences. P. regina, a non-model organism, is one of the most common forensically relevant insects in North America and is typically used to assist in estimating postmortem intervals (PMI). To better understand the roles P. regina plays in the numerous research fields, we re-constructed its genome using next generation sequencing technologies. The focus was on generating a reference genome through de novo assembly of high-throughput short read sequences. Following assembly, genetic markers were identified in the form of microsatellites and single nucleotide polymorphisms (SNPs) to aid in future population genetic surveys of P. regina. A total 530 million 100 bp paired-end reads were obtained from five pooled male and female P. regina flies using the Illumina HiSeq2000 sequencing platform. A 524 Mbp draft genome was assembled using both sexes with 11,037 predicted genes. The draft reference genome assembled from this study provides an important resource for investigating the genetic diversity that exists between and among blow fly species; and empowers the understanding of their genetic basis in terms of adaptations, population structure and evolution. The genomic tools will facilitate the analysis of genome-wide studies using modern genomic techniques to boost a refined understanding of the evolutionary processes underlying genomic evolution between blow flies and other insect species.