Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 49 for “"feature representations"”.
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Towards robust and domain invariant feature representations in Deep Learning
… perception-based systems is to define and learn representations of the scene that are more robust and adaptive to several nuisance factors. Over the recent past, for a variety of tasks involving images, learned representations have been empirically shown to outperform handcrafted ones. However, …
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Understanding the effects of higher order sequence features on peptide MHC binding
… In this thesis we propose the use of sequence feature representations as a means of capturing and categorizing these factors, and we develop the theoretical framework and justification for their use. We then apply sequence feature representations to analyze data derived from yeast display …
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Representation and transfer learning using information-theoretic approximations
Learning informative and transferable feature representations is a key aspect of machine learning systems. Mutual information and Kullback-Leibler divergence are principled and very popular metrics to measure feature relevance and perform distribution matching, respectively. However, clean …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
… the theoretical properties of neural network representations learned across multiple tasks within a probabilistic framework, establishing conditions under which neural networks can recover canonical feature representations that reflect the underlying ground-truth data generating process. Our …
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Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks
… thesis describes a method of learning compact feature representations for real-time covariance estimation. A direct log-likelihood optimization technique is used to train a deep convolutional neural network to predict the covariance matrix of a Gaussian measurement model, given representative …
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Using Machine Learning for Description and Inference of Cyber Threats, Vulnerabilities, and Mitigations
… cyber threats. We experiment with different feature representations and subsets of the data, and show that machine learning and NLP can effectively classify edges between entries from different data sources as well as predict possible edge candidates. Experts agree that several of our …
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DEEP LEARNING FOR FOR LARGE-SCALE FACIAL EXPRESSION RECOGNITION IN THE WILD
… via label fusion in order to obtain better feature representations. Experiments on in-the-wild AffectNet as well as a collection of laboratory based FER datasets suggests that jointly learning both dimensional and categorical models of affect significantly reduce redundancy and computational …
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Benchmarking Methods For Predicting Phenotype Gene Associations
… on multiple protein interaction networks and feature representations. We empirically evaluate the performance of multiple prediction tasks using two evaluation experiments: cross-fold validation and the more stringent temporal holdout. We demonstrate that all of the prediction methods …
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Scalability and interpretability of graph neural networks for small molecules
… network (SMNN), which is designed to have all feature representations and weights be human interpretable. I show that this network can achieve competitive performance with common graph neural network baselines. I also show that the network is capable of learning features that allow for transfer …
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Detecting cells and analyzing their behaviors in microscopy images using deep neural networks
… of these advances is exploiting hierarchical feature representations by various deep learning models, instead of handcrafted features based on domain-specific knowledge.</p> <p>In the work presented in this dissertation, we are particularly interested in exploring the power of deep neural …
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Multi-theme sentiment analysis with sentiment shifting
… sentiment by learning embeddings (i.e., vector representations) for both themes and words, and derives the shifter effect learning algorithm by modeling the shifted sentiment in a logistic regression model. Extensive experiments have been conducted on Yelp business reviews and IMDB movie …
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A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils
… prior information in the form of learned image feature representations may be combined with noisy imaging data to produce high-resolution, artifact-free sensitivity profiles. An in-vivo experiment demonstrates the effectiveness of the proposed method. The relative SENSE reconstruction error for …
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Learning the Language of Antibody Hypervariability Through Biological Property Prediction
… and binding specificity. We demonstrate how our feature representations can be applied to the accurate prediction of an antibody’s local and global 3D structures, mutational effects on antigen binding specificity, as well as identification of its paratope. The scalability of AbMAP newly enables …
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Improving and Analyzing Model Merging Methods for Adaptation
… of combining models by averaging intermediate features, referred to as model merging, and propose a new direction for achieving collective model intelligence through what we call compatible specialization. Current methods for model merging, such as parameter and feature averaging, struggle to …
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Advances in discriminative dependency parsing
… second, the ability to use arbitrarily-defined feature representations. This thesis explores three advances in the field of discriminative dependency parsing. First, we show that the classic Matrix-Tree Theorem (Kirchhoff, 1847; Tutte, 1984) can be applied to the problem of non-projective …
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Effects of automated cartographic generalization on linear map features
… relates to manual cartographic methods and feature representation is analyzed. It is suggested that the nature of representation of linear features on maps be considered in the analysis of effectiveness of automated generalization. The development of a computer platform for evaluating linear …
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Inferring travel activity pattern from smartphone sensing data using deep learning
… algorithms provide a framework for learning feature representation from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other …
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Transfer learning algorithms for image classification
… be able to exploit complex high dimensional feature representations even when only a few labeled examples are available for training. To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) Exploit labeled data from related …
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Romance, revolution and regulation: colonialism and the US-Mexico border in American Cold War film.
… to focus on films of different genres which feature representations of the US-Mexico border. The thesis’ central contribution therefore lies in its assertion that a study which is attentive to cinematic space and focused on a particular cinematic location can provide new ways of understanding …
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How deep learning can help emotion recognition
… through pre-specified rules or hand-crafted features. However, in the last few years, learned feature representations have experienced a resurgence mainly due to the success of deep neural networks. In this dissertation, we highlight how deep neural networks, when applied to emotion …
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