The paper provides a concentration result and sample complexity for linear Monge mapping estimation and its application in domain adaptation.
problem Estimating the linear Monge mapping between distributions and its application in domain adaptation.
method The approach involves proving a concentration result and sample complexity for the linear mapping operator, and using it to derive a generalization bound for domain adaptation with optimal transport.
result The method achieves a sample complexity of n−1/2 and approaches the performance of theoretical Bayes predictor under mild conditions. Estimates optimal transport maps with known cost functions.
problem Ensuring optimal transport maps correspond to real-world usefulness.
method Differentiable neural ground costs with known Monge map forms.
result General approach for incorporating prior information.
Paper introduces a novel map learning algorithm for domain translation and adaptation.
problem Learning a map between related data spaces that can be applied to out-of-sample data and satisfies application-specific constraints.
method Utilizes normalizing flows to parameterize a map that minimizes a probability distance and application-specific regularizers, solving a modified optimal transport problem.
result The proposed method (parOT) outperforms existing optimal transport approaches in domain adaptation and translation tasks.
VFMs use noise adapters to conditionally generate images in one step.
problem Conditional image generation with iterative models is slow and requires explicit sampling paths.
method Developed a variational flow map framework that learns noise distributions for conditional sampling.
result VFMs achieve well-calibrated conditional samples in a single forward pass.
Bayesian method improves SOM training for dynamic data.
problem Training Self Organizing Maps (SOM) for non-stationary data.
method Variational Bayesian approach with adaptive neighborhood radius.
result Method outperforms other adaptive methods in high dimensions.
TSC uses HMC and adaptive transport maps to optimize forward KL for variational inference.
problem Variational inference underestimates uncertainty when minimizing reverse KL.
method TSC uses Hamiltonian Monte Carlo and adaptive transport maps to optimize KL(p||q).
result TSC achieves competitive performance in training variational autoencoders on large-scale data.
CMA-ME combines CMA-ES and MAP-Elites for better quality and diversity in continuous domains.
problem Finding a diverse set of high-quality solutions in complex continuous domains.
method Combines CMA-ES self-adaptation with MAP-Elites archiving and mapping.
result CMA-ME outperforms MAP-Elites in both quality and diversity of solutions.
Framework adapts to new tasks based on prior knowledge.
problem Models struggle to adapt to novel tasks without direct experience.
method Learned task representations and meta-mappings to transform them.
result Meta-mapping achieves 80-90% performance on novel tasks.
New approach allows deep learning to adapt to new tasks without explicit training.
problem How to reuse deep learning knowledge for new tasks without explicit training.
method Homoiconic Meta-Mapping (HoMM) that transforms task representations.
result Zero-shot remapping of behavior to adapt to new tasks.
Proposes m-POT to improve m-OT's misspecified mappings issue.
problem Misspecified mappings in mini-batch optimal transport.
method Partial optimal transport (POT) between mini-batch empirical measures.
result m-POT alleviates incorrect mappings compared to current methods.
Adapts a short argument to derive a stability theorem for smooth maps.
problem Proving stability of smooth proper maps.
method Adapting a short argument from Golubitsky and Guillemin to derive the Mather stability theorem.
result Derives the Mather stability theorem from the Mather stability theorem in [MaII].
Optimal transport aligns source and target distributions for linear regression in 2D.
problem Domain adaptation for linear regression in 2D with limited target data.
method Combining K-means and optimal transport for estimating geometric transformations.
result Optimal transport recovers geometric transformations like rotations, translations, and homotheties.
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
problem Classifying hyperspectral images with limited labeled data.
method Shape-adaptive Reconstruction (SaR) for pixel preprocessing, SVM for probability estimation, and Smoothed Total Variation (STV) for denoising.
result SaR-SVM-STV outperforms SVM-STV with fewer labeled data.
AI-assisted interviews allow respondents to describe experiences naturally, but mapping those accounts into structured survey variables is fallible.
problem Mapping AI-assisted interview responses into structured survey variables is fallible.
method Adaptive Matrix Validation (AMV) is proposed, which involves mapping responses into tabular data and using a small set of structured questions for statistical adjustment.
result The estimator calibrates mapped values using validation answers from other respondents and corrects remaining error with validation answers observed for the target respondent.
LRS-DAG improves domain adaptation for low-resource settings.
problem Maintaining performance on source domain after target domain adaptation.
method Adds encoder layers to map target to source domain, maintaining source performance.
result Outperforms fine-tuning on synthetic low-resource datasets.
Optimal transport for functional data using Hilbert-Schmidt operators.
problem Optimal transport for distributions on function spaces with partially represented stochastic maps.
method Regularization technique to restrict transport maps to Hilbert-Schmidt operators, developing an efficient algorithm.
result Existence, uniqueness, and consistency of the Hilbert-Schmidt operator estimate for the transport map.
Deep learning for integrating diverse clinical measurements.
problem Combining data from different measurement instruments in longitudinal clinical registries.
method Domain adaptation using deep learning for mapping items from different instruments.
result Domain adaptation can recover latent trajectories even with limited data and misalignment.
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative meth…
Enhances ocean floor mapping with adaptive uncertainty estimates.
problem Inaccurate bathymetric data for precise ocean modeling.
method Block-based conformal prediction with VQ-VAE architecture.
result Significant improvements in reconstruction quality and uncertainty estimation reliability.
In this paper we find a unique normal form for the symplectic matrix representation of the conjugacy class of a prime order element of the mapping-class group. We find a set of generators for the fundamental group of a surface with a conformal automorphism of prime order which reflects the action the automorphism in an…
New energy identity found for biharmonic maps into spheres.
problem Establishing energy identity for biharmonic maps in supercritical dimensions.
method Adapting Lin-Rivière's strategy for sphere-valued maps.
result Energy identity for stationary biharmonic maps into spheres in supercritical dimensions n≥5. ACNet improves CNNs by adaptively connecting feature nodes.
problem Improving traditional CNNs for better performance and handling non-Euclidean data.
method Adaptively connects feature nodes to switch between global and local inference.
result ACNet achieves state-of-the-art performance and overcomes limitations of MLP and CNN.
Study complex structures on geodesic and harmonic map spaces.
problem Existence and uniqueness of compatible complex structures.
method Definition and study of adapted complex and involutive structures.
result Existence and uniqueness of such structures are established.
Automates API mapping across languages with minimal manual effort.
problem Manual effort required to identify parallel program corpora and API mappings.
method Combines domain adaptation and code embedding with GANs to align vector spaces of APIs.
result Automatically identifies cross-language API mappings with less prior knowledge.
Sourcerer uses deep learning to map land cover from limited labeled data.
problem Producing accurate land cover maps with scarce labeled data.
method Bayesian-inspired, deep learning approach with a novel regularizer.
result Sourcerer outperforms other methods, even with minimal labeled target data.
Two adaptive kernel selection methods improve the accuracy of Kernelized Diffusion Maps.
problem Selecting an appropriate kernel for Kernelized Diffusion Maps.
method Two complementary approaches: variational outer loop and unsupervised cross-validation.
result Both methods improve the quality and stability of the recovered eigenfunctions.
Method maps imperfect simulations to observed stellar spectra using unsupervised domain adaptation.
problem Mapping from large sets of imperfect simulations and observational data.
method Adversarial autoencoders, cycle-consistency constraint, and generative surrogate physics emulator network.
result Reconstructed spectra quality and discovery of new spectral features.
Constructs a moment map flow for isotropic maps on surfaces.
problem Understanding isotropic maps on surfaces and their properties.
method Develops a Kähler moment map geometry and a modified moment map flow.
result Polyhedral modified moment map flow induces a strong deformation retraction.
JSCN improves cross-domain recommendation by learning domain-invariant user representations.
problem Cross-domain recommendation data sparsity and domain-incompatibility issues.
method JSCN uses multi-layer spectral convolutions on different graphs to learn domain-invariant user representations and domain adaptive user mappings.
result Significant improvement in cross-domain recommendation performance (9.2% recall, 36.4% MAP improvements).
A new SOM method learns from both labeled and unlabeled data.
problem Improving performance in semi-supervised learning.
method Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM).
result ALTSS-SOM outperforms other methods in classification and clustering.
Generative Adapter adapts LMs with a single forward pass, reducing inference overhead.
problem Efficient adaptation of large language models for new contexts.
method Generative Adapter directly maps new contexts to low-rank LM adapters via self-supervised learning.
result Significant reduction in inference overhead with no need for fine-tuning.
We present a proof due to Duistermaat that the gradient flow of the norm squared of the moment map defines a deformation retract of the appropriate piece of the manifold onto the zero level set of the moment map. Duistermaat's proof is an adaptation of Lojasiewicz's argument for analytic functions to functions which ar…
CAM-GAN improves GANs for continual learning with efficient feature map transformations.
problem Efficient continual learning for GANs with reduced parameter growth.
method Designing and leveraging parameter-efficient feature map transformations, including global and task-specific parameters, residual bias, and Fisher information matrix.
result Significantly improved model performance and high-quality samples with fewer parameters.
Let Ξ be the crown domain associated with a non-compact irreducible hermitian symmetric space G/K. We give an explicit description of the unique G-invariant adapted hyper-Kähler structure on Ξ, i.e. compatible with the adapted complex structure Jad and with the G-invariant Kähle…
Optimal transport on SPD matrices improves domain adaptation for BCI.
problem Improving domain adaptation between two domains using SPD matrices.
method Modelled domain difference as diffeomorphism, used polar factorization theorem for optimal transport, applied weighted Riemannian mean.
result Demonstrated state-of-the-art performance on BCI data sets.
Adaptive algorithm improves nonlinear data assimilation for non-Gaussian systems.
problem Challenges of non-Gaussian statistics in data assimilation.
method Triangular measure transport with P-spline basis functions and an information criterion.
result Automatic selection of parsimonious parametrization for efficient adaptation.
This paper adapts submersions, immersions, and étale maps to diffeology.
problem Providing suitable analogs for submersions, immersions, and étale maps in diffeology.
method Nonlinear approach to diffeological submersions, immersions, and étale maps.
result Characterization and properties of diffeological embeddings and étale maps.
Improved saliency maps for deep neural networks with reduced noise.
problem Noisy explanations in Integrated Gradients for deep neural networks.
method SmoothTaylor, adaptive noising, and SmoothGrad techniques.
result SmoothTaylor and adaptive noising generate better quality saliency maps.
Optimal transport aligns rotated linear regression models across domains.
problem Aligning rotated linear regression models across domains with differing statistical properties.
method Combines K-means clustering, OT, and SVD to estimate rotation angle and adapt regression model.
result Optimal transport map recovers underlying rotation in R2. New flow connects symplectic maps to hyperKähler geometry.
problem Understanding symplectic maps and their geometry.
method Established a correspondence between symplectic diffeomorphisms and hyperKähler moment maps.
result Introduced a new flow, the modified moment map flow.
We affirmatively address the question of whether the proposed link homotopy invariant ω of Li is well-defined. It is also shown that if one wishes to adapt the homotopy invariant τ of Schneiderman-Teichner to a link homotopy invariant of link maps, the result coincides with ω.
This paper presents generalized momentum mappings for covariant Hamiltonian field theories. The new momentum mappings arise from a generalization of symplectic geometry to LVY, the bundle of vertically adapted linear frames over the bundle of field configurations Y. Specifically, the generalized field momentum obs…
Recently there has been renewed interest in the mapping-class group of a compact surface of genus g≥2 and also in its finite order elements. A finite order element of the mapping-class group will be a conformal automorphisms on some Riemann surface of genus g. Here we give the details of the proof that there is…
A new method for online multi-label stream classification.
problem Challenges in classifying continuous data streams with concept drift and delayed labels.
method Online unsupervised incremental method based on self-organizing maps.
result The method is highly competitive in both stationary and concept drift scenarios.
When learning a mapping from an input space to an output space, the assumption that the sample distribution of the training data is the same as that of the test data is often violated. Unsupervised domain shift methods adapt the learned function in order to correct for this shift. Previous work has focused on utilizing…
We develop a 2D travel time tomography method which regularizes the inversion by modeling groups of slowness pixels from discrete slowness maps, called patches, as sparse linear combinations of atoms from a dictionary. We propose to use dictionary learning during the inversion to adapt dictionaries to specific slowness…
End-to-end speaker recognition method using neural networks.
problem Speaker and session variability in speaker verification.
method Joint Factor Analysis with tied hidden variables, MAP adaptation, two-step backpropagation.
result Improved likelihood ratios and robust performance on RSR2015 database.
Algorithm adapts to non-stationary rewards without prior knowledge.
problem Optimizing decisions in non-stationary environments without prior knowledge of changes.
method Optimization-based algorithm that restarts when non-stationarity is detected.
result Achieves tighter dynamic regret bound and is nearly minimax optimal.