FEAT estimates free energy using adaptive transports.
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Extends denoising and score estimation to energy models via Tweedie's formula.
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
New estimates for Hitchin's equations at high energy.
Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify an explicit density. However, this limitation can be overcome by instead using …
We derive gradient and energy estimates for critical points of the full supersymmetric sigma model and discuss several applications.
This paper generalizes neural transport learning for free energy estimation in arbitrary state spaces.
Estimates for Schrödinger operators on manifolds with bounded Ricci curvature.
Study of charged scalar fields on Reissner-Nordström spacetimes via energy estimates.
Gradient estimation techniques applied to programs with randomness in high energy physics.
New model estimates Gibbs free energies using machine learning and isobaric-isothermal flows.
Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately achieved by Markov chain Monte Carlo methods, but may still face a formidable obs…
The paper improves energy decay estimates for Dir-stationary Q-valued functions and applies them to Liouville-type theorems and continuity.
We prove a conformally invariant estimate for the index of Schrödinger operators acting on vector bundles over four-manifolds, related to the classical Cwikel-Lieb-Rozenblum estimate. Applied to Yang-Mills connections we obtain a bound for the index in terms of its energy which is conformally invariant, and captures th…
Study on infinite energy maps from surfaces to CAT(0) spaces.
The paper proves optimal estimates and inequalities for spectral functions on certain manifolds.
In this paper, we study Lichnerowicz type estimate for eigenvalues of drifting Laplacian operator and L1 and L2 energy for drifting heat equation on closed manifolds with weighted measure. In some sense, this study is about the eigenvalue estimate on Ricci solitons.
The Möbius energy is one of the knot energies, and is named after its Möbius invariant property. It is known to have several different expressions. One is in terms of the cosine of conformal angle, and is called the cosine formula. Another is the decomposition into Möbius invariant parts, called the decomposed Möbius e…
An energy estimate is proved for the Bel--Robinson energy along a constant mean curvature foliation in a spatially compact vacuum spacetime, assuming an bound on the second fundamental form, and a bound on a spacetime version of Bel--Robinson energy.
In this paper, we analyze energy-harvesting adaptive diffusion networks for a distributed estimation problem. In order to wisely manage the available energy resources, we propose a scheme where a censoring algorithm is jointly applied over the diffusion strategy. An energy-aware variation of a diffusion algorithm is us…
Paper excludes the lowest energy level as an accumulation point for harmonic maps into analytic manifolds.
Study fractional Allen-Cahn equation and nonlocal minimal surfaces, improving energy and perimeter estimates.
A neural network model minimizes region-based free energy for faster inference in MRFs.
A key issue in the estimation of energy hedges is the hedgers' attitude towards risk which is encapsulated in the form of the hedgers' utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses this issue by estimating an…
New invariant for 4D hypersurfaces ensures smooth critical points.
Density estimation is a fundamental problem in statistical learning. This problem is especially challenging for complex high-dimensional data due to the curse of dimensionality. A promising solution to this problem is given here in an inference-free hierarchical framework that is built on score matching. We revisit the…
Bidirectional bounds stabilize training of energy-based models.
We show that the energy density of critical points of a class of conformally invariant variational problems with small energy on the unit 2-disk B_1 lies in the local Hardy space h^1(B_1). As a corollary we obtain a new proof of the energy convexity and uniqueness result for weakly harmonic maps with small energy on B_…
New inequality shows energy growth and decay in geometric problems.
Previously, Cristofaro-Gardiner, Hutchings and Ramos have proved that embedded contact homology (ECH) capacities can recover the volume of a contact 3-manifod in their paper "the asymptotics of ECH capacities" . There were two main steps to proving this theorem: The first step used an estimate for the energy of min-max…
Study bounds CMC surface index in 3-manifolds using energy.
A physically natural potential energy for simple closed curves in is shown to be invariant under Möbius transformations. This leads to the rapid resolution of several open problems: round circles are precisely the absolute minima for energy; there is a minimum energy threshold below which knotting cannot oc…
Study proposes a nonlocal approximation of the Willmore functional using fractional Allen-Cahn energies.
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences, and has since inspired a huge body of related methods that use it as an integral building block. Being an importance sampling based estimator, however, FEP suffers from a severe limitation: …
A new loss function ED simplifies training energy-based models without scores.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
Proves energy estimates for tensorial wave equations, decoupling components for stability proof.
Characterizes complex Hessian equations for bounded energy functions.
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive…
The paper establishes scattering theory for wave equations on Schwarzschild spacetime.
We prove the energy identity for min-max sequences of the Sacks-Uhlenbeck and the biharmonic approximation of harmonic maps from surfaces into general target manifolds. The proof relies on Hopf-differential type estimates for the two approximations and on estimates for the concentration radius of bubbles.
Uniform volume estimate for Kähler metrics in big cohomology classes.
The paper analyzes the emergence of almost-honeycomb structures in low-energy planar clusters.
Study proposes initial data sets for solving gravitational equations, proving energy estimates.
This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be tra…
The paper proves rigidity estimates for varifolds almost minimizing the Willmore energy.
Novel framework for learning infinitesimal generator of stochastic processes.
We show that the non pluripolar product of positive currents is a bimeromorphic invariant. Under some natural assumptions, we show that the (weighted) energy associated to big cohomology classes are also bimeromorphic invariants. We compare the weighted energy functionals of currents with respect to different cohomolog…