VD
V. Dakov
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Prior specification is a fundamental challenge in Bayesian inference. Traditionally, a prior represents a belief over the behavior of a statistical model based on expert knowledge. Such knowledge is not always available, or can be hard to express in a numerical form. As an answer, this thesis introduces the Prior-Learning Prior-Fitted Network (PLPFN), a transformer-based meta-learning model that amortizes prior learning. The model takes in a set of related tasks and outputs fully Bayesian uncertainty estimates over prior parameters in a single forward pass. The novelty lies in providing interpretable prior parameter outputs and examining the prior learning problem through the lens of hierarchical modeling and identifiability. The framework is evaluated on two representative prior structures: Bayesian Linear Regression and a Hierarchical Gaussian Process. The results show that PLPFN's approximations remain close to asymptotically exact baselines, such as closed-form conjugate prior learning and Markov Chain Monte Carlo. Applying the learned priors to Bayesian Optimization shows that the PLPFN is competitive with state-of-the-art prior learning methods in terms of sample efficiency and final regret, both in- and out-of-distribution. The PLPFN framework is a valuable step in shifting prior specification in Bayesian inference from the complex manual translation of beliefs to specifying a set of related datasets.
...
Prior specification is a fundamental challenge in Bayesian inference. Traditionally, a prior represents a belief over the behavior of a statistical model based on expert knowledge. Such knowledge is not always available, or can be hard to express in a numerical form. As an answer, this thesis introduces the Prior-Learning Prior-Fitted Network (PLPFN), a transformer-based meta-learning model that amortizes prior learning. The model takes in a set of related tasks and outputs fully Bayesian uncertainty estimates over prior parameters in a single forward pass. The novelty lies in providing interpretable prior parameter outputs and examining the prior learning problem through the lens of hierarchical modeling and identifiability. The framework is evaluated on two representative prior structures: Bayesian Linear Regression and a Hierarchical Gaussian Process. The results show that PLPFN's approximations remain close to asymptotically exact baselines, such as closed-form conjugate prior learning and Markov Chain Monte Carlo. Applying the learned priors to Bayesian Optimization shows that the PLPFN is competitive with state-of-the-art prior learning methods in terms of sample efficiency and final regret, both in- and out-of-distribution. The PLPFN framework is a valuable step in shifting prior specification in Bayesian inference from the complex manual translation of beliefs to specifying a set of related datasets.
Change detection with remote sensing data highlights se- mantic differences in an area between two or more time intervals. It involves the comparison of aerial photographs of the same location taken some time apart. This faci itates mass scale analysis of urban and rural data over time, including population trends, city expansion trends and illegal building detection. State-of-the-art methods for the task are predominantly deep learning networks, following an encoder-decoder architecture. These architectures all share the trait of having a ”fusion” point - a location in the network where inputs transition from being processed independently to becoming correlated. F sions can be classified in three categories: early, middle and late, depending on how deep within the network they occur. This study aims to show how changing the fusion impacts the size, spread and number of changes detected. It is motivated by how the receptive field of feature maps in convolutional neural networks expands in deeper layers, extracting features with different complexities. For this, four fusion architectures on three different datasets are compared: LEVIR-CD, HiUCD and a new, fully-controled dataset, CSCD. In terms of test accuracy and the changes’ size and spread, results are inconclusive. Which fusion achieves the highest performance varies per dataset. Possible reasons why include the complexity of remote sensing data and general differences between areas, but this is a subject of further study. The only conclusive category is the number of changes detected. On aver- age, all architectures overestimate the number of changes in a scene. When the accuracy of architectures is com- parable, however, early fusion overestimates the number of objects changed the most, while middle and late fusion give more realistic estimates. The case study has room for refinement in problem isolation, more data and extending the problem towards more architectures, but is a promising step towards understanding fusion.
...
Change detection with remote sensing data highlights se- mantic differences in an area between two or more time intervals. It involves the comparison of aerial photographs of the same location taken some time apart. This faci itates mass scale analysis of urban and rural data over time, including population trends, city expansion trends and illegal building detection. State-of-the-art methods for the task are predominantly deep learning networks, following an encoder-decoder architecture. These architectures all share the trait of having a ”fusion” point - a location in the network where inputs transition from being processed independently to becoming correlated. F sions can be classified in three categories: early, middle and late, depending on how deep within the network they occur. This study aims to show how changing the fusion impacts the size, spread and number of changes detected. It is motivated by how the receptive field of feature maps in convolutional neural networks expands in deeper layers, extracting features with different complexities. For this, four fusion architectures on three different datasets are compared: LEVIR-CD, HiUCD and a new, fully-controled dataset, CSCD. In terms of test accuracy and the changes’ size and spread, results are inconclusive. Which fusion achieves the highest performance varies per dataset. Possible reasons why include the complexity of remote sensing data and general differences between areas, but this is a subject of further study. The only conclusive category is the number of changes detected. On aver- age, all architectures overestimate the number of changes in a scene. When the accuracy of architectures is com- parable, however, early fusion overestimates the number of objects changed the most, while middle and late fusion give more realistic estimates. The case study has room for refinement in problem isolation, more data and extending the problem towards more architectures, but is a promising step towards understanding fusion.