| 1. |
- Ambroladze, Amiran, et al.
(författare)
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When is small beautiful?
- 2003
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Ingår i: Lecture Notes in Computer Science (Learning Theory and Kernel Machines). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783540407201 ; 2777, s. 729-730
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Konferensbidrag (refereegranskat)abstract
- The basic bound on the generalisation error of a PAC learner makes the assumption that a consistent hypothesis exists. This makes it appropriate to apply the method only in the case where we have a guarantee that a consistent hypothesis can be found, something that is rarely possible in real applications. The same problem arises if we decide not to use a hypothesis unless its error is below a prespecified number.
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| 2. |
- Khatami, Mohammad, et al.
(författare)
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BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI
- 2017
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Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203. ; 63, s. 593-600
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Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.
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| 3. |
- Tigas, Panagiotis, et al.
(författare)
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Interventions, Where and How? : Experimental Design for Causal Models at Scale
- 2022
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Ingår i: Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. - : Neural information processing systems foundation.
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Konferensbidrag (refereegranskat)abstract
- Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. This work incorporates recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, allowing for active causal discovery of large, nonlinear SCMs while selecting both the interventional target and the value. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-Rènyi, Scale Free) for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.
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| 4. |
- Xian, R. Patrick, et al.
(författare)
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A machine learning route between band mapping and band structure
- 2023
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Ingår i: Nature Computational Science. - : Springer Nature. - 2662-8457. ; 3:1, s. 101-114
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Tidskriftsartikel (refereegranskat)abstract
- The electronic band structure and crystal structure are the two complementary identifiers of solid-state materials. Although convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting the quasiparticle dispersion (closely related to band structure) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, here we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band-structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data.
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