arXiv research

ASA improves ASR by adapting SD models to SI model's deep feature distribution.

Let $(M,g,\si)$ be a compact spin manifold of dimension $n \geq 2$. Let $λ_1^+(\tilde{g})$ be the smallest positive eigenvalue of the Dirac operator in the metric $\tilde{g} \in [g]$ conformal to $g$. We then define $\lamin(M,[g],\si) = \inf_{\tilde{g} \in [g]} λ_1^+(\tilde{g}) \Vol(M,\tilde{g})^{1/n} $. We show that $…

Develops a more powerful selective inference method for stepwise feature selection.

New algorithm improves signal reconstruction from noisy measurements with side information.

The moduli space of holomorphic fiber bundles ${\cal M}_n(\Si)$ over a compact Riemann surface $\Si$ is considered. A formula for the regularised determinant and an other for the symplectic form at trivial bundle are proposed.

Develops new reinforcement learning methods for complex constrained decision-making problems.

Deep model learns coupled representations from side information for sparse signal recovery.

New method reduces computational cost for selective inference.

LSI enables joint learning of latent variables and generative models.

Enhances selective inference for generalized lasso using parametric programming.

This paper ranks pre-trained DNNs using a novel SI measure.

SIS-RNN improves model flexibility for sequential data.

New method predicts unobserved interactions between sets of elements.

Paper extends SI method for detecting CPs in complex systems' frequency domain.

SI-CLAD improves clustering-based anomaly detection by controlling false positives.

Paper proposes a new method for selective inference in robust regression.

Proposes a new method to improve selective inference for Lasso models.

Let SI(S_g) denote the hyperelliptic Torelli group of a closed surface S_g of genus g. This is the subgroup of the mapping class group of S_g consisting of elements that act trivially on H_1(S_g;Z) and that commute with some fixed hyperelliptic involution of S_g. We prove that the cohomological dimension of SI(S_g) is …

A k-submanifold L of an open n-manifold M is called weakly integrable (WI) [resp. strongly integrable (SI)] if there exists a submersion Φ:M\to R^{n-k} such that L\subset Φ^{-1}(0) [resp. L= Φ^{-1}(0)]. In this work we study the following problem, first stated in a particular case by Costa et al. (Invent. Math. 1988): …

The assumption in the main result of [Peter W. Michor: Basic Differential Forms for Actions of Lie Groups, Proc. AMS 124, 5 (1996) 1633-1642] is removed. Thus: A section of a Riemannian $G$-manifold $M$ is a closed submanifold $\Si$ which meets each orbit orthogonally. It is shown that the algebra of $G$-invariant diff…

STAND-DA improves AD in DA target domains with limited data.

In this paper we consider on a complete Riemannian manifold $M$ an immersed totally geodesic hypersurface $\Si$ existing together with an immersed submanifold $N$ without focal points. No curvature condition is needed. We obtained several connectedness results relating the topologies of $M$ and $\Si$ which depend on th…

A quantum circuit designed for efficient statistical model preparation and training.

We study minimal graphic functions on complete Riemannian manifolds $\Si$ with non-negative Ricci curvature, Euclidean volume growth and quadratic curvature decay. We derive global bounds for the gradients for minimal graphic functions of linear growth only on one side. Then we can obtain a Liouville type theorem with …

Paper presents a fast and adaptive filter for SI suppression in full-duplex transceivers.

This work uses a SI-DNN to predict AC-OPF solutions efficiently.

Study uses RNN to detect CPs with SI to control false positives.

SETrLUSI combines diverse knowledge from multiple domains for faster convergence.

We analyze symmetries in overparametrized neural networks using a mean-field approach.

Study on second homology group of genus 3 hyperelliptic Torelli group.

Paper introduces PTL-SI for statistical inference in TL-HDR, controlling FPR.

In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is sufficiently rich, the SI method is a useful learning algorithm. In particular, we show that larger data imbalance, a quality that is typically per…

Starting with a Lie algebroid ${\cal A}$ over a space $M$ we lift its action to the canonical transformations on the affine bundle ${\cal R}$ over the cotangent bundle $T^*M$. Such lifts are classified by the first cohomology $H^1({\cal A})$. The resulting object is a Hamiltonian algebroid ${\cal A}^H$ over ${\cal R}$ …

We define an integer graded symplectic Floer cohomology and a spectral sequence which are new invariants for monotone Lagrangian sub-manifolds and exact isotopies. Such an integer graded Floer cohomology is an integral lifting of the usual Floer-Oh cohomology with $Z_{\Si (L)}$ grading. As one of applications of the sp…

Developed accurate empirical potentials for Si:H nanowires using multi-fidelity Gaussian process.

A novel text-independent speaker identification (SI) method is proposed. This method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic information among adjacent frames as feature sets to capture speaker's characteristics. In order to utilize dynamic information, we design super-MFCCs features by cas…

Unified understanding of three continual learning regularisation methods.

New functions derived from arrow diagrams for spherical curves, invariant under certain deformations.

A new method reduces feature screening cost from $O(np)$ to $O(\sqrt{n}p)$.

Let $(M,g)$ be a compact Riemannian manifold of dimension $n\geq 3$. For a metric $g$ on $M$, we let $\la_2(g)$ be the second eigenvalue of the Yamabe operator $L_g:= \frac{4(n-1)}{n-2} Δ_g + \scal_g$. Then, the second Yamabe invariant is defined as $$ \si_2(M) \definedas \sup \inf_{h \in [g]} \la_2(h) \Vol(M,h)^{2/n}.…

Sharp thresholds and contiguity for community detection in contextual SBM.

Proposes a two-step method for sound source separation.

Let M be a compact manifold equipped with a Riemannian metric g and a spin structure \si. We let $λ(M,[g],\si)= \inf_{\tilde{g} \in [g]} λ_1^+(\tilde{g}) Vol(M,\tilde{g})^{1/n}$ where $λ_1^+(\tilde{g})$ is the smallest positive eigenvalue of the Dirac operator D in the metric $\tilde{g}$. A previous result stated that …

Paper introduces a method to assess the statistical reliability of changepoints using selective inference and dynamic programming.

DC-SIS selects features faster than mRMR for Parkinson's vocal diagnosis.

Paper improves VAEs using Monte Carlo methods.

Paper develops a new model for dynamic graph representation learning.

This paper proposes RAS, a novel unsupervised loss function for speech separation.