Two new transversality theorems for linearly perturbed mappings are proven.
problem Understanding the behavior of mappings under small perturbations.
method Proves two transversality theorems for generic linearly perturbed Cr mappings. result Establishes new theorems for the stability of mappings under small changes.
Polynomial-time algorithm learns MAP perturbation models for structured prediction.
problem Efficiently learning parameters for robust structured prediction models.
method Minimizes Rademacher-based generalization bound to learn parameters in polynomial time.
result Guaranteed generalization to unseen examples under certain conditions.
New non-existence results for harmonic maps into perturbed cones.
problem Proper harmonic maps into perturbed cones in \(\mathbb{R}^n\), horospheres in \(\mathbb{H}^n\).
method Extension of foliated maximum principle to non-compact settings.
result New non-existence results for proper harmonic maps.
If we consider the moduli space of flat connections of a non trivial principal SO(3)-bundle over a surface, then we can define a map from the set of perturbed closed geodesics, below a given energy level, into families of perturbed Yang-Mills connections depending on a small parameter. In this paper we show that this m…
Paper proves existence of smooth nontrivial Dirac-harmonic maps.
problem Existence of nontrivial Dirac-harmonic maps from closed surfaces.
method Proves existence using ε-regularity and perturbations.
result Existence of smooth nontrivial Dirac-harmonic maps.
Graph cuts find global optima for Potts models in slight perturbations.
problem Finding optimal solutions in Potts models with graph cuts.
method α-expansion algorithm for MAP inference, with certification for perturbations.
result All local minima are global minima in slight perturbations, and solutions are close to original.
Efficiently learns perturb-and-map models using weighted log-likelihood.
problem Structured output prediction with weighted Hamming losses.
method Generalizes perturb-and-MAP framework, uses dynamic graph cuts for MAP inference, and double stochastic gradient descent for efficient learning.
result Shows efficiency in learning log-supermodular models with weak supervision.
DANCE improves saliency maps by adding subtle input variations.
problem Poor performance of saliency methods in saturated gradients, adversarial perturbations, and inter-feature dependence.
method Two-step procedure: 1) Perturbation mechanism, 2) Aggregation of saliency maps.
result DANCE saliency method outperforms existing methods qualitatively and quantitatively.
Proves conditions for complexification of real maps and their homology.
problem Conditions for homology and homotopy equivalence of real and complex maps.
method Analyzes local behavior of singular points and proves necessary conditions.
result Proves a conjecture about good real perturbations and their homotopy equivalence.
A new approach to maximum likelihood learning of discrete graphical models and RBM in particular is introduced. Our method, Perturb and Descend (PD) is inspired by two ideas (I) perturb and MAP method for sampling (II) learning by Contrastive Divergence minimization. In contrast to perturb and MAP, PD leverages trainin…
New LP method recovers MAP solution from noisy stable instances.
problem MAP inference on noisy stable instances.
method Designing an algorithm to find nearby perturbation stable instances and using LP relaxation.
result LP approximately recovers the MAP solution from noisy stable instances.
The pentagram map's limit point is related to infinitesimal perturbations of polygons.
problem Understanding the limit point of the pentagram map and its relation to polygon perturbations.
method Interpreting Glick's operator as the infinitesimal monodromy of a polygon.
result Glick's operator measures the extent to which a perturbed polygon does not close up.
This paper presents a new approach, called perturb-max, for high-dimensional statistical inference that is based on applying random perturbations followed by optimization. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for the input. A classic re…
Deep neural networks improve free energy calculations for peptide conformations.
problem Challenges in developing suitable mappings for free energy perturbation.
method Adapted machine learning approach to train deep neural networks for mapping between Boltzmann distributions.
result Accurate free energy differences calculated between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å.
New methods use transport maps to improve Langevin dynamics for sampling.
problem Sampling high-dimensional, non-Gaussian distributions efficiently.
method Apply transport maps to accelerate Langevin dynamics convergence.
result Discretized processes converge to target distribution with non-asymptotic bounds.
Let N (resp., U) be a manifold (resp., an open subset of Rm). Let f:N→U and F:U→Rℓ be an immersion and a C∞ mapping, respectively. Generally, the composition F∘f does not necessarily yield a mapping transverse to a given subfiber-bundle of J1(N,Rℓ)…
Researchers construct a gauge-invariant energy functional for axially symmetric perturbations around Kerr black holes.
problem Understanding energy of axially symmetric perturbations around Kerr black holes.
method Hamiltonian dimensional reduction to a 2+1 Einstein-wave map system, constructing a positive-definite, gauge-invariant energy functional. result The energy functional serves as a Hamiltonian for the constrained evolution of linear perturbations.
We show that a small perturbation of the boundary distance function of a simple Finsler metric on the n-disc is also the boundary distance function of some Finsler metric. (Simple metric form an open class containing all flat metrics.) The lens map is map that sends the exit vector to the entry vector as a geodesic c…
Bayesian optimisation generates saliency maps for black-box models.
problem Generating saliency maps for models without access to parameters.
method Bayesian optimisation sampling method to find global salient regions.
result Approach outperforms grid-based methods and performs similarly to gradient-based methods.
New method creates universal perturbations to fool neural network interpretations.
problem Vulnerability of gradient-based saliency maps to adversarial perturbations.
method Gradient-based optimization and PCA-based approach to create UPI.
result Existence and successful application of Universal Perturbation for Interpretation (UPI).
In this short note, we prove that conformal classes which are small perturbations of a product conformal class on a product with a standard sphere admit a metric extremal for some Laplace eigenvalue. As part of the arguments we obtain perturbed harmonic maps with constant density.
PMP improves sampling and learning in complex energy models.
problem Intractability of MAP computation in EBMs.
method Perturb-and-max-product (PMP) for parallel and scalable sampling and learning.
result PMP outperforms existing methods in various models, including Ising and RBMs.
In this paper we will perturb the scalar curvature of compact Kahler manifolds by incorporating it with higher Chern forms, and then show that the perturbed scalar curvature has many common properties with the unperturbed scalar curvature. In particular the perturbed scalar curvature becomes a moment map, with respect …
This work proves Kerr black holes are dynamically stable under certain perturbations.
problem Dynamical stability of Kerr black holes under axially symmetric perturbations.
method Dimensional reduction to 2+1 Einstein-wave map system, construction of positive-definite energy functional, proving boundary terms vanish.
result Strictly conserved positive energy for axially symmetric linear perturbations of Kerr black holes.
Study on financial systems using perturbed unimodal maps with heteroscedastic noise.
problem Analyzing systemic risk in financial systems using mathematical models.
method Investigation of one-dimensional unimodal maps perturbed by heteroscedastic noise, proving stability, convergence, and Lyapunov exponent continuity.
result Continuous dependence of average Lyapunov exponent on Markov chain parameters, and Gumbel's law for extreme values.
Stability of cut locus under metric perturbations in compact Riemannian manifolds.
problem Stability of cut locus under C2-perturbations of the metric. method Proving stability with respect to the Hausdorff metric of the cut locus under C2 perturbation of the metric. result The Hausdorff distance between cut loci converges to zero as the metrics converge.
Study of harmonic maps and instantons in 4D.
problem Constructing non-spin, simply-connected Ricci-flat 4-manifolds.
method Non-perturbative approach ruling out conical singularities from axisymmetric harmonic maps.
result Systematic counterexamples to Riemannian black hole uniqueness conjecture.
One pixel can significantly alter deep neural network outputs, revealing propagation patterns and vulnerability hotspots.
problem Understanding how a single pixel modification affects deep neural networks.
method Propagation Maps and locality analysis to visualize and understand the impact of pixel modifications.
result One pixel modifications can propagate through deep networks, affecting the final output and revealing vulnerability patterns.
Paper proposes structured semantic perturbations to improve adversarial attacks.
problem Vulnerability of deep neural networks to adversarial attacks.
method Manipulates semantic attributes via disentangled latent codes.
result Demonstrates the effectiveness of structured semantic perturbations.
In his celebrated paper "Generic projections", John Mather has shown that almost all linear projections from a submanifold of a vector space into a subspace are transverse with respect to a given modular submanifold. In this paper, an improvement of Mather's result is stated. Namely, we show that almost all linear pert…
In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the c…
Harmonic maps stability under small perturbations of boundary data.
problem Stability of minimising harmonic maps under small perturbations of boundary data.
method Analysis of W1,p perturbations of boundary data and energy minimisers. result Energy minimisers close to the original map in Hölder norm.
We prove that in many cases the existence of an extremal metric for some Laplace eigenvalue in a conformal class allows to find extremal metrics in conformal classes close by. As a consequence and as part of the arguments we obtain perturbed harmonic maps with constant density.
Generative Intervention Models predict perturbation effects without knowing the underlying mechanisms.
problem Predicting perturbation effects when the mechanisms are unknown.
method Generative Intervention Models (GIM) that map perturbation features to distributions over atomic interventions in a causal model.
result GIMs achieve robust out-of-distribution predictions and infer underlying perturbation mechanisms.
We come up with infinite-dimensional prequantum line bundles and moment map interpretations of three different sets of equations - the generalised Monge-Amp`ere equation, the almost Hitchin system, and the Calabi-Yang-Mills equations. These are all perturbations of already existing equations. Our construction for the g…
Inserts proximal mapping into deep networks for better regularization.
problem Effective regularization of deep learning models to handle adversarial perturbations and correlations between modalities.
method Proposes a new layer that directly produces regularized hidden layer outputs using proximal mapping.
result Outperforms state-of-the-art methods in robust temporal learning and multiview modeling.
Study local perturbations of vector bundles with polynomial curvature solutions.
problem Existence and stability of solutions to geometric PDEs under deformations.
method Geometric invariant theory, moment map framework, polystability conditions.
result Existence and uniqueness of solutions under local polystability conditions.
Study magnetic perturbations in Riemannian and Lorentzian Calderón problems.
problem Determining metrics from boundary measurements under magnetic perturbations.
method Runge approximation for Riemannian case, microlocal analysis for Lorentzian case.
result Metrics can be uniquely determined in both Riemannian and Lorentzian cases under specific perturbations.
We obtain relations among the characteristic classes of a manifold M admitting corank one maps. Our relations yield strong restrictions on the cobordism class of M and also nonexistence results for singular maps of the projective spaces. We obtain our results through blowing up a manifold along the singular set of a sm…
Researchers found a way to measure energy in black hole perturbations.
problem Lack of positive-definite and conserved energy in black hole stability.
method Dimensional reduction and construction of a positive-definite energy functional.
result Conserved Hamiltonian energy for axially symmetric perturbations of Kerr black holes.
Method differentiates diffusion model training to predict sample sensitivity.
problem Predict how diffusion model samples change with small perturbations.
method Closed-form procedure for computing directional derivatives of the map.
result Estimates sensitivity of diffusion model samples to additive perturbations.
Modifying the method of [21], we compute the perturbed HF+ for some special classes of fibered three manifolds in the second highest spinc-structures Sg−2. The special classes considered in this paper include the mapping tori of Dehn twists along a single non-separating curve and along a transverse pair of c…
Develops a new attack model to better capture structural information in adversarial examples.
problem Lp norm-based adversarial attacks fail to capture structural information in input images.
method Structured Adversarial Attack (StrAttack) using ADMM framework to achieve strong group sparsity.
result StrAttack achieves strong group sparsity in adversarial perturbations with similar Lp norm distortion.
Quantum trace map defines invariants for knots and links, confirming a length conjecture.
problem Defining invariants for knots and links in hyperbolic 3-manifolds.
method Introducing a quantum trace map for ideally triangulated knot complements, combining with state-integral models.
result Perturbative invariants determine an asymptotic expansion of the Jones polynomial, confirming the length conjecture.
Proposes a stochastic optimization method for feature attribution.
problem Improving feature attribution methods for complex models.
method Reformulates the optimization problem as a differentiable function solvable by gradient-based algorithms, particularly stochastic optimization.
result The proposed method effectively identifies relevant parts of images.
New tool for interpreting neural nets, explaining model behavior.
problem Interpreting neural network responses and explaining model behavior.
method Full-Gradient representation and FullGrad saliency map approximation.
result FullGrad method explains model behavior more comprehensively and accurately than other methods.
New method evaluates visual explanations of deep models using adversarial perturbations.
problem Lack of objective evaluation of visual explanations of deep models.
method Proposes an adversarial perturbation approach to evaluate visual explanations of deep models.
result Demonstrates the effectiveness of the proposed approach through comparisons with existing methods.
Critical points of approximations of the Dirichlet energy à la Sacks-Uhlenbeck are known to converge to harmonic maps in a suitable sense. However, we show that not every harmonic map can be approximated by critical points of such perturbed energies. Indeed, we prove that constant maps and the rotations of S2 are th…