We establish the Thom isomorphism in twisted K-theory for any real vector bundle and develop the push-forward map in twisted K-theory for any differentiable proper map (not necessarily K-oriented). The push-forward map generalizes the push-forward map in ordinary K-theory for any -oriented differentiable…
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48 results for “push-forward maps”
This paper explores the nonconvexity of push-forward constraints in machine learning.
problem The nonconvexity of push-forward constraints in machine learning.
method The paper provides sufficient and necessary conditions for the (non)convexity of push-forward functions and maps.
result Push-forward constraints are generally nonconvex, which limits the design of convex optimization problems in machine learning.
Study embeddings between Barron spaces with various activation functions, focusing on RePU.
problem Understanding the influence of activation functions on infinitely wide neural networks.
method Prove embeddings by constructing push-forward maps on measures representing functions.
result Barron spaces with RePU activation have a hierarchical structure similar to Sobolev spaces.
In this note we apply heat kernels to derive some localization formula in sympletcic geometry, to study moduli spaces of flat connections on a Riemann surface, to obtain the push-forward measures for certain maps between Lie groups and to solve equations in finite groups.
In this note, we reconcile two approaches that have been used to construct stringy multiplications. The pushing forward after pulling back that has been used to give a global stringy extension of the functors K_0,K^{top},A^*,H^* [CR, FG, AGV, JKK2], and the pulling back after having pushed forward, which we have previo…
We give a proof of the cobordism invariance of the index of elliptic pseudodifferential operators on sigma-compact manifolds, where, in the non-compact case, the operators are assumed to be multiplication outside a compact set. We show that, if the principal symbol class of such an elliptic operator on the boundary of …
The purpose of this paper is to study the action of the mapping class group on the moduli space of representations of the fundamental group of a non-orientable surface into SU(2). The action is shown to be ergodic with respect to a natural measure. This measure is defined using the push-forward measure associated to a …
Push-forward models struggle to fit multimodal distributions due to high Lipschitz constants.
problem Expressivity of push-forward generative models in fitting multimodal distributions.
method Analyzing the Lipschitz constant and its relation to the total variation distance and Kullback-Leibler divergence.
result Push-forward models require high Lipschitz constants to approximate multimodal distributions, leading to a trade-off between expressivity and stability.
We discuss the fundamental (relative) 3-classes of knots (or hyperbolic links), and provide diagrammatic descriptions of the push-forwards with respect to every link-group representation. The point is an observation of a bridge between the relative group homology and quandle homology from the viewpoints of Inoue--Kabay…
Teichmüller space realized as symplectic quotient.
problem Realizing Teichmüller space as a symplectic quotient.
method Infinite-dimensional symplectic manifold, volume-preserving diffeomorphisms, momentum map.
result Teichmüller space and moduli space realized as symplectic orbit reduced spaces.
The Singular Asymptotics Lemma by Brüning and Seeley and the Push-Forward Theorem by Melrose lie at the very heart of their respective approaches to singular analysis. We review both and show that they deal with the same basic problem, giving solutions that emphasize different aspects of it. This also points to a possi…
Let G be a compact, simply connected Lie group. We develop a `quantization functor' from pre-quantized quasi-Hamiltonian G-spaces at level k to the fusion ring (Verlinde algebra) R_k(G). The quantization Q(M) is defined as a push-forward in twisted equivariant K-homology. It may be computed by a fixed point formula, si…
The paper shows how Sobolev maps affect currents in metric spaces.
problem Understanding how Sobolev maps affect currents in metric spaces.
method Proving that a Sobolev map pushes almost every compactly supported integral current to an Ambrosio-Kirchheim integral current.
result The paper proves an isoperimetric inequality for Sobolev mappings relative to bounded, closed, and additive cochains.
This research examines the geometry of latent spaces in push-forward generative models.
problem Tendency of deep generative models to output samples outside target distribution support.
method Geometric measure theory and truncation method to enforce simplicial cluster structure.
result Proves sufficient condition for optimality in latent space geometry.
Using optimal transport we study some dynamical properties of expanding circle maps acting on measures by push-forward. Using the definition of the tangent space to the space of measures introduced by Gigli, their derivative at the unique absolutely continuous invariant measure is computed. In particular it is shown th…
A new method uses normalizing flows to approximate optimal transport between empirical distributions.
problem Learning an optimal transport map between two empirical distributions.
method Relaxing the Monge formulation of optimal transport, using normalizing flows to approximate the solution.
result The method provides a good approximation of the true optimal transport.
We construct differential equivariant K-theory of representable smooth orbifolds as a ring valued functor with the usual properties of a differential extension of a cohomology theory. For proper submersions (with smooth fibres) we construct a push-forward map in differential equivariant K-theory. Finally, we construct …
Let denote the identity connected component of the real orthogonal group with signature . We give a complete description of the spaces of continuous and generalized translation- and -invariant valuations, generalizing Hadwiger's classification of Euclidean isometry-invari…
Proves a formula for push-forward of polynomial Chern forms in universal vector bundles.
problem Positivity of characteristic forms in vector bundles.
method Explicit computation of Chern curvature and use of flag bundles.
result Positivity of polynomials in Chern forms for Griffiths semipositive bundles.
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filterstat.ML
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles are pushed forward from the prior to the posterior density using a sequence of mappings that minimizes the Kullback-Leibler divergence between…
Study shows how to detect representation extendability using conformal measures.
problem Detecting extendability of representations using conformal measures.
method Using higher rank conformal measures and self-joinings of groups.
result Affirmative answer to detect extendability of representations.
Let M be a compact Riemannian manifold and let ,d be the associated measure and distance on M. Robert McCann obtained, generalizing results for the Euclidean case by Yann Brenier, the polar factorization of Borel maps S : M -> M pushing forward to a measure : each S factors uniquely a.e. into the composition …
We define Gromov--Witten invariants of exploded manifolds. The technical heart of this paper is a construction of a virtual fundamental class of any Kuranishi category (which is a simplified, more general version of an embedded Kuranishi structure.) We also show how to integrate differential…
Transport Quasi-Monte Carlomath.NA
Improves QMC for complex distributions using transport maps.
problem Challenges in applying QMC to general target distributions.
method Train a transport map to approximate target distributions, ensuring RQMC achieves superior error rates.
result Transport QMC achieves faster convergence rates than standard Monte Carlo under mild conditions.
Generalized differential cohomology theories, in particular differential K-theory (often called "smooth K-theory"), are becoming an important tool in differential geometry and in mathematical physics. In this survey, we describe the developments of the recent decades in this area. In particular, we discuss axiomatic ch…
This paper proposes a new method for conditional sampling using optimal transport.
problem Sampling conditional distributions in Bayesian inference and density estimation.
method Iterative block-triangular transport maps solving an optimal transport problem with a weighted L2 cost function.
result The proposed method extends the data-driven approach for conditional sampling.
SOS programming verifies MTW tensor non-negativity for optimal transport maps.
problem Verifying MTW tensor non-negativity for general cost functions is difficult.
method Sum-of-Squares (SOS) programming for verifying and approximating MTW non-negativity.
result SOS programming provides certificates and approximations of MTW non-negativity.
The paper proves a Hard Lefschetz Theorem for push-forwards of polarized twistor modules.
problem Proving a Hard Lefschetz Theorem for push-forwards of polarized twistor modules.
method Using Kashiwara and Kawai's theorem on Hodge structures and regular polarized twistor modules.
result Proves the Hard Lefschetz Theorem for push-forwards of polarized twistor modules.
We investigate the low-dimensional structure of deterministic transformations between random variables, i.e., transport maps between probability measures. In the context of statistics and machine learning, these transformations can be used to couple a tractable "reference" measure (e.g., a standard Gaussian) with a tar…
This work proposes a new method to match distributions across different spaces using cycle-consistent maps.
problem Matching distributions across different spaces with consistent bidirectional maps.
method A novel unbalanced Monge optimal transport formulation for matching distributions on different spaces, employing cycle-consistent maps.
result The proposed discrepancy captures the cycle-consistent GAN framework and provides theoretical support.
The study defines differential forms and currents on orbifolds with corners.
problem Defining differential forms and currents on orbifolds with corners.
method Using the formalism of étale proper groupoids with corners, the authors provide constructions and proofs without orbifold charts.
result The Fréchet space of differential forms and the dual space of currents are independent of the chosen groupoid representation.
A new method called TemperFlow tackles multimodality in sampling from unnormalized distributions.
problem Sampling from unnormalized distributions with isolated modes.
method TemperFlow learns a sequence of tempered distributions to progressively approach the target distribution.
result TemperFlow overcomes the limitations of existing methods and achieves superior performance.
Flexible selective inference using flow-based transport maps.
problem Selective inference with complex selection events.
method Flow-based generative modeling for conditional distribution approximation.
result Valid p-values and confidence sets for adaptively selected hypotheses and parameters.
Statistical Bergman geometrymath.CV
The paper connects Bergman geometry with information geometry.
problem Exploring the Bergman geometry of complex domains.
method Introducing a mapping Φ and using Fisher information metrics.
result Established a new statistical curvature formula for the Bergman metric.
The paper proves a convergence theorem for Wiener measures on holonomy groups.
problem Understanding convergence of Wiener measures on holonomy groups.
method Using stochastic parallel transports along convergent metric connections.
result Proves a convergence theorem for push-forward Wiener measures on holonomy groups.
Develops a new method for sampling from Bayesian credible sets using deep generative quantile learning.
problem Sampling from posterior distributions in high-dimensional spaces with intractable likelihoods.
method Uses deep neural networks to implicitly sample from Bayesian credible sets via a push-forward mapping and Monge-Kantorovich depth.
result Demonstrates improved performance and theoretical consistency of the quantile learning framework.
Novel algorithm solves optimal transport using evolving probability distributions and convolution.
problem Sample-based optimal transport problem.
method Adversarial formulation with convolution of adaptive kernel and evolving measure.
result Algorithm robust to dimensionality and produces complex maps.
Optimal schedules improve transport map approximation and learning.
problem Improving the approximation and learning of transport maps.
method Optimal scheduling of transport maps to minimize spatial Lipschitz constant.
result The optimal schedule can be computed in closed form and results in a significantly smaller Lipschitz constant.
A new method maps high-dimensional Bayesian inverse problems to lower dimensions.
problem High-dimensional Bayesian inverse problems with complex prior information.
method Data-driven VAE prior and KRnet map for posterior approximation in latent space.
result Efficiently reduces computational cost and approximates posterior distributions.
Accelerating the identification of informative reduced representations of proteins with deep learning for graphsphysics.comp-ph
Deep learning speeds up protein mapping entropy calculation.
problem Efficiently calculating the mapping entropy of protein structures.
method Deep graph networks for accelerating mapping entropy computation.
result Deep graph networks achieve a speedup factor of up to 10^5.
In this paper, for a compact Lie group action,we prove the anomaly formula and the functoriality of the equivariant Bismut-Cheeger eta forms with perturbation operators when the equivariant family index vanishes. In order to prove them, we extend the Melrose-Piazza spectral section and its main properties to the equiva…
New EOT solvers estimate both plans and maps efficiently.
problem Difficulty in tuning entropic regularization strength in EOT solvers.
method Time discretization and proper parameter scheduling to optimize EOT computation.
result ProgOT is faster and more robust, outperforming neural networks.
A new method to estimate optimal transport maps without constraints.
problem Challenges in fitting optimal transport maps with neural networks.
method Introducing a Monge gap regularizer to estimate OT maps without architectural constraints.
result The proposed method significantly outperforms other baselines in practice.
We explain how to define the quantization of q-Hamiltonian SU(2)-spaces as push-forwards in twisted K-homology, and prove a `quantization commutes with reduction' theorem for this setting. As applications, we show how the Verlinde formulas for flat SU(2) or SO(3) bundles are obtained by localization in twisted K-homolo…
Transformers preserve support and can approximate any continuous map.
problem Understanding the mathematical properties of transformers.
method Characterizing maps between measures that can be represented as transformers and proving their properties.
result Transformers preserve support and have uniformly continuous Fréchet derivatives.
Constructs small bundle gerbes and proves index theorems for manifolds.
problem Constructing and analyzing bundle gerbes on manifolds.
method Defines and constructs small bundle gerbes, uses pseudodifferential and semiclassical smoothing operators, proves index theorems.
result Proves the Atiyah-Singer type theorem for small bundle gerbes, showing their relation to twisted K-theory.
We introduce the notion of volume of the representation variety of a finitely presented discrete group in a compact Lie group using the push-forward measure associated to a map defined by a presentation of the discrete group. We show that the volume thus defined is invariant under the Andrews-Curtis moves of the genera…
The study constructs bundles with non-multiplicative A-genus and finds non-trivial homotopy groups in spaces of metrics.
problem Locating non-trivial rational homotopy groups in spaces of metrics with lower curvature bounds.
method Constructs smooth bundles with non-vanishing A-genus and uses them to find homotopy groups.
result Non-trivial homotopy groups in spaces of metrics with lower curvature bounds.