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.
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Showing 1 to 20 of 44 for “"Cross Entropy."”.
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Finding a Viable Neural Network Architecture for Use with Upper Limb Prosthetics
… architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy convolutional neural network, which was …
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Differential Games of Mixed Strategies in a Variational Inference Framework: An Application to the Perimeter Defense Problem
… Min-Max SV-MPC on one hand while we extends the Cross-Entropy optimization method to a Min-Max Cross-Entropy optimization method. Moreover, we demonstrate the approach using robots as players in the perime- ter defense problem in which multiple defenders are tasked to protect a high- value target …
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Novel Instance-Level Weighted Loss Function for Imbalanced Learning
… learning algorithms minimize the binary cross-entropy objective function to determine the final parameter estimates. This objective function assumes an equal class distribution between the minority (i.e. events) and majority (i.e. non-events) classes, which almost never exists in …
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Network reliability estimation
… Sampling technique, a new technique called the Cross-Entropy method has been developed in the late 90's to find the optimal Importance Sampling parameters. By employing the Cross-Entropy technique, we propose a new scheme called the Merge Process with Cross-Entropy. The new scheme improves the …
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Exploring Loss Functions in Machine Learning
… loss functions and their applications. Softmax Cross-Entropy Loss, stands as a prevalent choice in neural network classification tasks. It treats all misclassifications uniformly. However, multi-class classification problems often have many semantically similar classes. We should expect that …
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Surface Realization Using a Featurized Syntactic Statistical Language Model
… and evaluated. Generation accuracies and cross-entropy for the new language model did not beat the comparison bigram language model.
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Consistent Estimators for Learning to Defer to an Expert
… for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks.
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Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis
… 4: State Dependent Importance Sampling with Cross Entropy for Heavy-tailed Systems Chapter 5: Stochastic Insurance-Reinsurance Networks: Modeling, Analysis and Efficient Monte Carlo
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Neural Network Supervision: Notes on Loss Functions, Labels and Confidence Estimation
… update rule that generalises the standard cross-entropy gradient, and allows directly controlling the relative effect of easy and hard examples on the training process. We show that the proposed update rule cannot be derived by using a loss function and yields better classification accuracy …
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Classification of underwater pipeline events using deep convolutional neural networks
… results obtained in the test set, accuracy and cross entropy curves obtained in the validation set during training are analyzed, so that the performances of each method and for each event class are compared. Visualizations of the convolutional neural network intermediate layer outputs are also …
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Essays on Maximum Entropy Principle With Applications to Econometrics and Finance
… portfolio selection problems using the maximum entropy (ME) principle. Since an entropy measure turns out to be a distance measure between two distributions, it can be used to estimate unknown density function. Entropy can be also interpreted as a measure of the degree of diversification and …
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Contrastive representation learning for bioimage quantification
… be interpreted as a temperature scaled energy cross-entropy, and I discuss its hard positive and hard negative mining properties. The conducted experiments demonstrate on two domains its superior performance over soft target and standard cross-entropy as well as InfoNCE. The thrid and final …
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Towards General-purpose Vision via Multiview Contrastive Learning
… which rivals or even beats the supervised cross-entropy learning on standard image classification benchmarks. In the last part, we discuss other applications (such as knowledge distillation) and improvements of multiview contrastive learning (e.g., how to improve its efficiency on uncurated …
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Entropy-based machine learning algorithms applied to genomics and pattern recognition
… of TFs. The CDT models are trained using the Cross Entropy (CE) optimization method, a Monte Carlo optimization method based on concepts from information theory related to statistical mechanics. We then further study the CDT model as a general pattern recognition and transfer learning …
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Artificial intelligence-enabled transaction prediction.
… The Adam algorithm combined with binary cross-entropy as the loss function was applied to a 70% training, 20% validation, and 10% split to improve stability and generalization. Our proposed approach demonstrated a high accuracy of 0.93, outperforming traditional models like decision tree …
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Domain adaptation for neural machine translation
… forgetting in NMT domain adaptation across multiple sequential domains. We develop a robust variant of Minimum Risk Training which allows more beneficial use of small, highly domain-specific tuning sets than simple cross-entropy fine-tuning, and can mitigate exposure bias resulting …
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A Framework for Hybrid Intrusion Detection Systems
… Those included in this thesis are SQL injection, cross-site scripting, directory traversal and remote file inclusion. This work identifies common and existing data breach detection methods as well as the necessary improvements for IDS models. Ultimately, the proposed approach combines an anomaly …
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Learning speech embeddings for speaker adaptation and speech understanding
… maximize the F-measure instead of conventional cross-entropy minimization and show that it is possible to increase the number of classes with nonzero recall. In the last two chapters, we provide additional discussions on the impact of these projects from both technical and social perspectives, …
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Triplet entropy loss: improving the generalisation of short speech language identification systems
… The research also introduces the novel Triplet Entropy Loss training method. This training method involves training a network simultaneously using Cross Entropy and Triplet loss. Several tests were run with three different CNN architectures to investigate what the effect all three of these …
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Towards More Interpretable AI With Sparse Autoencoders
… models demonstrate remarkable capabilities across diverse domains, the specific representations and algorithms they learn remain largely unknown. The quest to understand these mechanisms holds dual significance: scientifically, it represents a fundamental inquiry into the principles …
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