A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Invertible DenseNets improve model efficiency and performance.
This work presents a novel approach to train invertible linear layers by adding rank-one perturbations.
The paper connects Brauer algebra homology to symmetric group homology.
i-DenseNets improve parameter efficiency and performance in density estimation.
ISR creates analytical relationships from data via invertible maps.
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called graph residual flow…
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a cloud-specific neural network. To capture the similarity between point clouds we rely o…
Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure t…
Kernelised flows improve density estimation and generation with fewer parameters.
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions. These simulators enable modelers to capture effects that are difficult to characterize analytically or su…
In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of param…
We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces seq…
New CycleGAN uses invertible generator for faster, less resource-intensive CT denoising.
Hydrogen atom confined in an inverted-Gaussian potential, with detailed numerical methods and results.
Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formal…
Study on estimating invertible functions with minimax analysis.
Local invertibility of higher order tensor transforms on compact manifolds.
This paper proposes a new method to quantify uncertainty in reservoir characterization using invertible neural networks.
Study of strongly invertible Legendrian links in contact 3-space.
Method estimates parameters of complex nonlinear systems.
We consider the problem of recovering material parameters in a transversely isotropic medium from the qP and qSV waves' travel times, given the axis of isotropy and the material parameters associated to the qSH wave speed. The operators obtained from the pseudolinearization argument are of parabolic type, and so we dis…
Study on invariant Seifert surfaces for strongly invertible knots, showing large gaps in genus.
Dirac operator invertibility proven for specific manifolds.
Table of symmetric diagrams for knots up to 10 crossings.
The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a cruci…
Local invertibility of ray transforms on convex manifolds.
This work tackles exploding inverses in INNs, revealing and mitigating their numerical non-invertibility.
CF-INNs can approximate any invertible function, resolving a long-standing problem.
Develops equivariant grid homology for strongly invertible knots.
New findings on knot genera using advanced techniques.
Defines knot signature invariant using G-signature theorem.
BayesFlow learns complex models using neural networks.
Paper shows invertibility of tensor X-ray transform on certain manifolds.
Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately reconstruct the inputs to a layer from its outputs, thus has the potential to unravel the black-box model. An invertible network classifier c…
For operators of many different kinds it has been proved that (generalized) Darboux transformations can be built using so called Wronskian formulae. Such Darboux transformations are not invertible in the sense that the corresponding mappings of the operator kernels are not invertible. The only known invertible ones wer…
Paper accelerates K-means clustering for large sparse document data.
The paper improves generative models to avoid replicating observed examples.
New invertible transformations improve flow-based generative models.
We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requires adding a simple …
ButterflyFlow uses butterfly matrices for efficient invertible layers in normalizing flows.
By using parity arguments we prove that free knots are, generally, not invertible.
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measuremen…
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation. While such a transformation is invertible, its Jacobian is very sparse and thus may lack expressiveness. Th…
Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints are needed for enforci…
Two knots with unique surgery properties.
We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable …
By a result of John Ball (1981), a locally orientation preserving Sobolev map is almost everywhere globally invertible whenever its boundary values admit a homeomorphic extension. As shown here for any dimension, the conclusions of Ball's theorem and related results can be reached while completely avoiding the problem …
New spectral sequences define knot invariants.