Paper combines machine learning and model averaging for robust parameter estimation.
problem Estimating structural parameters with partially unknown functional forms.
method Pairing double/debiased machine learning with stacking for model averaging.
result DDML with stacking is more robust to unknown functional forms than single learners.
Constructs moduli stacks for quiver connections and extends non-Abelian Hodge theory.
problem Extending non-Abelian Hodge theory to moduli stacks of quiver connections.
method Formalizes and constructs moduli stacks of bundles with λ-connections over prestacks.
result Shows moduli stacks are algebraic and locally of finite presentation when base is smooth and projective.
Optimizes PCB stack-up design for high-speed circuits.
problem Efficiently optimize many parameters in PCB stack-up design.
method Parallel and intelligent Bayesian optimization for stripline design.
result Improves accuracy and efficiency of PCB stack-up optimization.
NN-Stacking improves predictive power of regression models by adjusting stacking coefficients with features.
problem Low predictive power of linear stacking methods.
method NN-Stacking uses neural networks to estimate adaptive stacking coefficients.
result NN-Stacking leads to better predictive power, especially in large datasets.
New neural stack and Turing Machine architectures prove stability and computational power.
problem Designing stable neural network architectures for Turing Machine simulation.
method Introducing neural stack and Turing Machine architectures, proving stability and computational equivalence.
result Differentiable nnTM with bounded neurons can simulate Turing Machine in real-time and is equivalent to UTM.
Improved prediction of soil parameters using Multi-target Stacked Generalisation on EDXRF spectra.
problem Challenges in predicting multiple soil parameters accurately from EDXRF spectra.
method Multi-target Stacked Generalisation (MTSG) method combining multiple regression models.
result MTSG significantly improved prediction accuracy for multiple soil parameters, reducing average error from 0.67 to 0.64.
Stacking improves deep neural network training efficiency.
problem Improving the efficiency of training deep neural networks.
method Proposes stacking as a form of accelerated gradient descent.
result Proves stacking provides accelerated training for certain deep linear residual networks.
New minimal surfaces show stacking disorder in periodic structures.
problem Reproducing experimental twinning defects in periodic minimal surfaces.
method Constructing non-periodic minimal surfaces that lift to disordered stacking in 3D.
result Reproduced twinning defects in periodic minimal surfaces as stacking disorder.
DSSCN improves lifelong learning of non-stationary data streams through adaptive network construction.
problem Lifelong learning of non-stationary data streams with efficient and adaptive models.
method Deep stacked stochastic configuration network (DSSCN) with self-constructing deep stacked network structure and adaptive hidden unit parameters.
result DSSCN outperforms existing data stream algorithms in continual learning of non-stationary data streams.
Proposes SVM-based Deep Stacking Network for improved deep learning.
problem Improving deep learning performance and interpretability.
method Uses stacked SVM classifiers within a DSN architecture and a BP-like layer tuning scheme.
result Demonstrates superior performance compared to benchmark models on image and text data.
StackNet learns new tasks without forgetting old ones.
problem Catastrophic forgetting in continual learning.
method StackNet combines an index module with a StackNet to retain old task performance.
result StackNet prevents performance degradation of previously learned tasks.
We construct and analyze minimal disc stackings with bounds on their Morse index.
problem Constructing and analyzing minimal free boundary disc stackings.
method Constructing minimal free boundary disc stackings in a three-dimensional Euclidean unit ball, proving bounds on their Morse index.
result Uniform, linear bounds on the Morse index of all such surfaces.
StackGenVis simplifies ensemble learning by visualizing model selection and performance.
problem Complexity in choosing and combining models for stacking ensemble learning.
method Visual analytics system that dynamically adapts performance metrics, manages data instances, selects algorithms, and measures predictive performance.
result Reduces complexity of stacking by removing overpromising and underperforming models.
New methods combine model predictions to avoid linear mixtures' limitations.
problem Combining predictions from different models to avoid linear mixtures' limitations.
method Log-linear pooling (locking) and quantum superposition (quacking) to optimise model weights.
result Demonstrated locking method with illustrative example and practical application.
Proposes a deep learning method to estimate virtual battery parameters from end-use measurements.
problem Estimating virtual battery parameters from limited load information.
method Transfer learning based stacked autoencoder for deep network framework.
result Effectively estimates virtual battery parameters for different load ensembles.
The paper analyzes how stacking improves model stability.
problem Lack of theoretical insight into how stacking works.
method Stability analysis of learning algorithms, focusing on hypothesis stability.
result The hypothesis stability of stacking is a product of base models and combiner.
Autostacker uses EA to evolve machine learning pipelines without domain knowledge.
problem Finding optimal machine learning models and hyperparameters without expert knowledge.
method Hierarchical stacking architecture + Evolutionary Algorithm (EA) for efficient parameter search.
result Autostacker achieves state-of-the-art performance on 15 datasets.
We review the basic definition of a stack and apply it to the topological and smooth settings. We then address two subtleties of the theory: the correct definition of a ``stack over a stack'' and the distinction between small stacks (which are algebraic objects) and large stacks (which are generalized spaces).
In this article, we derive many properties of étale stacks in various contexts, and prove that étale stacks may be characterized categorically as those stacks that arise as prolongations of stacks on a site of spaces and local homeomorphisms. Moreover, we show that the bicategory of étale differentiable stacks and loca…
New tool for summarizing time-varying data shapes.
problem Understanding dynamic data shapes.
method Introducing crocker stacks for time-varying metric spaces.
result Demonstrated utility in parameter identification task.
Constructs cohomology decompositions for symmetric stacks.
problem Cohomology of symmetric stacks.
method Constructs decompositions of cohomology, Borel--Moore homology, and vanishing cycle cohomology.
result Defines BPS cohomology and proves its equivalence to intersection cohomology for smooth stacks.
RNNs can store information in parameters and input history, with capacity and trainability being key factors.
problem Understanding and optimizing the capacity and trainability of RNNs.
method Experimental analysis of various RNN architectures, including comparison of training difficulty and per-task parameter capacity.
result RNNs can store an amount of task information linear in the number of parameters (approximately 5 bits per parameter) and can store one real number from input history per hidden unit.
Bayesian stacking improves model performance with varying model weights.
problem Improving model predictions with heterogeneous input performance.
method Bayesian hierarchical stacking with varying model weights inferred via Bayesian inference.
result Hierarchical stacking yields better predictions than linear averaging.
Generates biomedical abstracts from titles, years, and keywords.
problem Difficulties in understanding biomedical research papers due to specialized language.
method Conditional transformer-based language model with metadata conditioning.
result Generated abstracts are more relevant and non-trivial than GPT-2.
This thesis explores geometric stacks and Poisson manifolds, proving new results in their classification and equivalence.
problem Classifying and understanding geometric stacks and Poisson manifolds.
method Rigorous proofs and new site constructions for geometric stacks and Poisson manifolds.
result Classification and equivalence results for b-symplectic manifolds.
Relates discrete group actions to orbit spaces as differentiable stacks.
problem Understanding dynamics of discrete groups on manifolds.
method Relating discrete group actions to orbit spaces as differentiable stacks.
result Orbit stack encodes dynamics up to conjugation and inversion.
Develops theory of differential graded schemes for derived stacks.
problem Creating a theory for derived stacks using dg schemes.
method Formulates dg schemes as homotopy sites, equates to stacks on dg algebras.
result Infinity category of stacks represented by dg schemes is derived schemes.
We generalize the notion of a small sheaf of sets over a topological space or manifold to define the notion of a small stack of groupoids over an étale topological or differentiable stack. We then provide a construction analogous to the étalé space construction in this context, establishing an equivalence of 2-categori…
New category defined for differentiable stacks.
problem No specific problem stated; focuses on new definition.
method Introducing a new category definition.
result Established relation with Lie groupoid category.
Novel algorithms improve warfarin dose prediction accuracy.
problem Challenges in estimating warfarin dose due to narrow therapeutic index and individual variability.
method Stacked generalization frameworks combining different machine learning algorithms.
result Stacked algorithms outperform the IWPC MLR algorithm, especially in subgroups.
Smooth stacks of orbifolds are shown to be infinite-dimensional orbifolds.
problem Understanding the structure of Hom-stacks of orbifolds.
method Using Lie groupoids and Fréchet-Lie groupoids to represent Hom-stacks.
result Hom-stacks of orbifolds are infinite-dimensional orbifolds.
This work characterizes global quotient stacks---smooth stacks associated to a finite group acting a manifold---among smooth quotient stacks [M/G], where M is a smooth manifold equipped with a smooth proper action by a Lie group G. The characterization is described in terms of the action of the connected componen…
The BPS decomposition theorem splits cohomology of symmetric stacks into invariant parts.
problem Decomposing the cohomology of smooth symmetric stacks into invariant parts.
method Using cohomological Hall induction and intersection cohomology of moduli spaces.
result Establishes the BPS decomposition theorem for various symplectic stacks.
A dynamic stacking method for network node classification.
problem Node classification on networks with varying topological features.
method Dynamic functional coefficients for heterogeneous classifiers.
result Significantly more accurate model for network node classification.
Stacked conformal prediction simplifies model validation.
problem Validating stacked predictive models efficiently.
method Meta-learner at the top of a stacked ensemble for approximate marginal validity.
result The method achieves approximate marginal validity without a separate calibration sample.
Constructs equivariant cohomology models for differentiable stacks.
problem Developing cohomology theory for stacks with group actions.
method Extends classical results for smooth manifolds to differentiable stacks.
result Derives spectral sequences generalizing Bott's spectral sequence.
Associate stacks to Poisson manifolds for equivalence checking.
problem Equivalence checking of Poisson manifolds.
method Associate stacks to Poisson manifolds using Dirac manifolds and closed 2-forms.
result Two Poisson manifolds are equivalent if their associated stacks are isomorphic.
This paper explores the relationship between gerbes over stacks and Lie groupoid extensions.
problem Exploring the relationship between gerbes over stacks and Lie groupoid extensions.
method Defines a gerbe over a stack and explores its relationship with Lie groupoid extensions.
result Establishes the relationship between gerbes over stacks and Morita equivalence classes of Lie groupoid extensions.
Stacked Capsule Autoencoders reconstruct objects from images using part relationships.
problem Reconstructing objects from images with robustness to viewpoint changes.
method Two-stage unsupervised capsule autoencoder that predicts part templates and object capsules.
result State-of-the-art results for unsupervised classification on SVHN and MNIST.
In this paper, we consider diffeological spaces as stacks over the site of smooth manifolds, as well as the "underlying" diffeological space of any stack. More precisely, we consider diffeological spaces as so-called concrete sheaves and show that the Grothendieck construction sending these sheaves to stacks has a left…
Theory of Lie algebroids over stacks developed.
problem No specific problem stated; theory development for Lie algebroids over stacks.
method Extends Lie algebroid theory to differentiable stacks, defines categories, cohomology, and examples.
result Lie algebroids satisfy descent for submersions and have a cohomology theory.
Study connections on Lie groupoids and stacks using Atiyah sequences.
problem No specific problem stated; general connections on Lie groupoids and stacks.
method Construct connections using Atiyah sequences associated with transversal tangential distributions.
result Detailed study and construction of connections on Lie groupoids and stacks.
Constructs moduli stacks of quiver bundles and applies to Higgs bundles.
problem Classifying morphisms of vector bundles over a fixed base.
method General method for constructing moduli stacks of diagrams of vector bundles indexed by a simplicial set.
result Recovery of Nakajima quiver varieties and alternate construction of moduli stacks of Higgs bundles.
Tree-SMU enables strong compositional generalization in neural networks.
problem Zero-shot generalization to novel compositions of concepts.
method Tree Stack Memory Units (Tree-SMU) with Stack Memory Units (SMU).
result Tree-SMU achieves strong empirical results on mathematical reasoning benchmarks.
New potentials found for sheaves on Calabi-Yau 4-folds.
problem Understanding sheaves on Calabi-Yau 4-folds.
method Derived Quot-stacks and Lagrangian distributions.
result Globally defined −1-shifted potentials on sheaves. Extends Massey products to differential cohomology using stacks.
problem Tackles Massey products in differential cohomology.
method Uses stacks to extend Massey products from cohomology to differential cohomology.
result Shows how properties of extended Massey products relate to classical ones.
Describes Teichmüller stack structure near Kähler points.
problem Understanding Teichmüller spaces/stacks near Kähler points.
method Analyzes local structure of Teichmüller stack near Kähler points.
result Affirmative answer to Catanese Kur=Teich question at generic Kähler points.
StaPLR selects important views for multi-view learning models.
problem Selecting important views in multi-view learning to reduce data collection costs.
method Developed stacked penalized logistic regression (StaPLR) for view selection.
result StaPLR outperforms existing methods in view selection, reducing false positives.