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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 98 for “"Loss functions"”.
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Exploring Loss Functions in Machine Learning
<p>The loss function plays a critical role in machine learning. It is fundamental in training, evaluating, and optimizing machine learning models, directly impacting their effectiveness and efficiency in solving specific tasks. We explore three new loss functions and their applications. Softmax …
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Robustness of Consistent Loss Functions for Multinomial Outcome Models
… estimation, which uses the logarithmic loss function, is the default method used to estimate latent parameters consistently in multinomial outcome models. However, it is sensitive to even a tiny fraction of corruption in the training data. Alternatively, other loss functions in the …
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Neural Network Supervision: Notes on Loss Functions, Labels and Confidence Estimation
… update rules may replace the need for a loss function and its gradient. We introduce a parameter 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 …
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Optimal versus naive diversification : do different loss functions improve portfolio choice?
… different settings. In each setting, I vary the loss function used when estimating returns and covariances, length of the estimation window, and number of factors used in our estimation model. I find that when measuring performance by Sharpe ratio, choice of loss function strongly influences …
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Asymmetric Loss Functions and Combination of Forecasts with Applications in Equity Premium Prediction
… methods, called the Discount Asymmetric Square Loss Function (DASLF), the Discount Linex Loss Function (DLLF), the Discount Lin-Lin Loss Function (DLLLF) and the Discount General Entropy Loss Function (DGELF) are proposed to maximize forecast accuracy and boost the efficiency of forecasts …
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Analysis of loss functions in XVAE-GAN: A novel two-view image generation network
DSpace SAF Submission Ingestion Package generated from Vireo submission #14457 on 2020-02-28 at 17:35:35
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Probability measure estimation in positron emission tomography using loss functions based on Sobolev norms
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1994.
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Geometric aspects of uncertainty quantification in high-dimensional statistics
… this is critically affected by the choice of loss functions. Thus the problem can be understood from a geometric perspective. Specifically, under the sparse regression model, we layout results for foundational choices of loss functions while adding to the field by introducing the reweighted …
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I-Con: A Unifying Framework for Representation Learning
… there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of modern loss functions in machine learning. In particular, we introduce a framework that shows that …
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Imbalanced learning using actuarial modified loss function in tree-based models
… mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This thesis …
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Inference for the ratio of two exponential parameters using a Bayesian approach
… better in terms of average interval lengths. Loss functions will also be used to derive Bayes estimates. The squared error loss and all-or-nothing loss functions will be compared with each other through a simulation study. The performance of each loss function will be compared by looking at …
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Program Synthesis over Noisy Data
… as an optimization problem formulated over the loss of a candidate program over the noisy dataset and the complexity of the candidate program. I present a noisy program synthesis algorithm based on finite tree automaton. Results from an implemented system running this algorithm on problems from …
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On Semi-supervised Estimation of Distributions
… We adopt the minimax framework with [notation] loss functions, and we show that the composition of uni-variate minimax estimators achieves minimax risk with the optimal first-order constant for 𝑝 ≥ 2, in the regime 𝑚 = 𝑜(𝑛).
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Deep Learning For Microscopic Image Restoration
… problem, two new combinations of frequency based loss functions were proposed for denoising low SNR images. The first loss function is the combination of frequency and pixel-wise based losses. The second combination combines frequency domain with feature-based losses. These new loss functions were …
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Bayes Multiple Decision Functions: Theory, Computation and Application
… for this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for the use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) …
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Elektronen-Energieverlustspektroskopie von quasi-eindimensionalen Kupraten und Vanadaten
… cuprates and vanadates. Electron energy-loss spectroscopy in transmission was employed to measure the momentum-dependent loss function of Li2CuO2, CuGeO3, V2O5 and NaV2O5. The comparison between the experimental data and the results from bandstructure as well as cluster calculations …
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Learning image super resolution from joint examples
… both external and internal SR. We de ne the two loss functions using sparse coding and epitomic matching, respectively. A corresponding adaptive weight is constructed to balance their e ect according to the reconstruction errors. Various image results demonstrate the e ectiveness of the proposed …
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Complete classes of two-stage estimation procedures for certain finite sample problems
… which depends on the combined sample. The loss functions usually are linear combinations of loss due to terminal decision and loss due to sampling. They find the optimal second sample size as well as the optimal first sample size.
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On Machine Learning Loss Landscapes
Loss functions are pivotal to the training of every machine learning model. When evaluating the loss function over a large range of parameters, a loss landscape is obtained. In this thesis, loss landscapes for various classes of machine learning models are explored. Geometric properties of the loss …
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Fast Adaptive Laws for Adaptive Control Under Stochastic Disturbances
… gradient methods for the optimization of convex loss functions, and derive a new adaptive law designed for stable adaptive control. Second, we review the state of the literature on recursive least-squares adaptive laws - especially those with variable-direction forgetting factor - and we derive …
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