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.
Introduces TT-NF for more compact neural field representations.
Characterizes kernel of linearization for minimal surfaces problem
Relates quantum cohomology to tt*-Toda equations for minuscule flag manifolds.
Study geodesic X-ray transforms on hyperbolic surfaces, proposing new reconstruction methods.
Study of symplectic groupoids from tt*-Toda equations.
Solutions of tt*-equation from SU(2)_k fusion algebra.
Analyzes tt*-structures from -type Stokes data.
Constructs positive energy representations from Toda equations Stokes data.
In this paper, we prove a far-from-CMC result similar to the ones obtained by Holst, Nagy, Tsogtgerel and Maxwell for the conformal Einstein-scalar field constraint equations on compact Riemannian manifolds with positive (modified) Yamabe invariant.
Classifies Toda-type tt*-structures and their fixed points.
Using nonlinear pde techniques, we construct a new family of globally smooth tt* structures. This includes tt* structures associated to the (orbifold) quantum cohomology of a finite number of complex projective spaces and weighted projective spaces. The existence of such "magical solutions" of the tt* equations, namely…
The paper proves an isomorphism between structures of Landau-Ginzburg and Calabi-Yau models.
Recent studies have introduced end-to-end TTS, which integrates the production of context and acoustic features in statistical parametric speech synthesis. As a result, a single neural network replaced laborious feature engineering with automated feature learning. However, little is known about what types of context in…
Study on harmonic Higgs bundles with vanishing endormorphism and eigenvalues of Q.
Polynomial growth elements found in all subgroups of Out(F_n).
Explains quantum cohomology of Grassmannians using tt* equations.
Proves existence and uniqueness of solutions for A_n tt*-Toda equations.
We study transverse-tracefree (TT)-tensors on conformally flat 3-manifolds . The Cotton-York tensor linearized at maps every symmetric tracefree tensor into one which is TT. The question as to whether this is the general solution to the TT-condition is viewed as a cohomological problem within an elliptic com…
Solves constant pre-factor problem for tt*-Toda equations using asymptotic data and symplectic structures.
We propose , a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), it uses randomization to trade off exploration and exploitation. In the -armed bandit setting, we show that there are infinitel…
New surfaces with conjugate points have global blow-down maps in their TT spaces.
Proposes Textual Echo Cancellation to improve speech recognition.
New method uses TT approximations to solve HJB equations for efficient sampling.
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor product approximations -- in particular the tensor train (TT) format -- have become a valuable tool for the solution of large-scale problems …
Investigates neural TTS systems for Japanese and English.
End-to-end text-to-speech (TTS) synthesis is a method that directly converts input text to output acoustic features using a single network. A recent advance of end-to-end TTS is due to a key technique called attention mechanisms, and all successful methods proposed so far have been based on soft attention mechanisms. H…
We present a fully convolutional wav-to-wav network for converting between speakers' voices, without relying on text. Our network is based on an encoder-decoder architecture, where the encoder is pre-trained for the task of Automatic Speech Recognition, and a multi-speaker waveform decoder is trained to reconstruct the…
Bayesian tensor train method recovers streaming data with high accuracy.
We describe all smooth solutions of the two-function tt*-Toda equations (a version of the tt* equations, or equations for harmonic maps into SL(n,R)/SO(n)) in terms of (i) asymptotic data, (ii) holomorphic data, and (iii) monodromy data. This allows us to find all solutions with integral Stokes data. These include solu…
Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it…
In "Isomonodromy aspects of the tt* equations of Cecotti and Vafa I. Stokes data" (arxiv:1209.2045) we described all smooth solutions of the two-function tt*-Toda equations in terms of asymptotic data, holomorphic data, and monodromy data. In this supplementary article we focus on the holomorphic data and its interpret…
We show how to parameterise solutions of the general relativistic vector constraint equation on Einstein manifolds by unconstrained potentials. We provide a similar construction for the trace-free part of tensors satisfying the linearised scalar constraint. Previous work of ours has provided similar different construct…
A new TTS method uses diffusion and VAE for better speech synthesis.
End-to-end Sanskrit TTS developed with limited data, achieving good quality.
In this note we prove an existence result for the Einstein conformal constraint equations for metrics with vanishing Yamabe invariant assuming that the TT-tensor is small in .
We give an overview on the tt*-geometry defined for isolated hypersurface singularities and tame functions via Brieskorn lattices. We discuss nilpotent orbits in this context, as well as classifying spaces of Brieskorn lattices and (limits of) period maps.
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus of target utterances. We demonstrate that there does not exist a one-size-fits-all ada…
Tensor network surrogate for efficient option pricing in large portfolios.
TensorGuide improves LoRA efficiency and expressivity through joint tensor-train optimization.
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity and uniform Lipschitz continuity. This paper provides a comprehensive mechanism for computing the least squares estimator of a multivariate…
A new method computes Greeks for multi-asset options using tensor trains and Fourier transforms.
Establishes correspondence between Calabi-Yau and Landau-Ginzburg structures.
We introduce a novel random projection technique for efficiently reducing the dimension of very high-dimensional tensors. Building upon classical results on Gaussian random projections and Johnson-Lindenstrauss transforms~(JLT), we propose two tensorized random projection maps relying on the tensor train~(TT) and CP de…
For any triple , where W is a closed connected and oriented 3-manifold, L is a link in W and is a flat principal B-bundle over W (B is the Borel subgroup of $SL(2,\mc)$), one constructs a $\Dd$-scissors congruence class $\cG_{\Dd}(W,L,ρ)$ which belongs to a (pre)-Bloch group $\Pp (\Dd)$. The class $\cG_{\D…
In this note, we prove that for a cobounded,Lipschitz path $γ:I\to\TT$, if the pull back bundle over is a strongly relatively hyperbolic metric space then there exists a geodesic in $\TT$ such that and are close to each other.
We give a physical explanation of the Kontsevich-Soibelman wall-crossing formula for the BPS spectrum in Seiberg-Witten theories. In the process we give an exact description of the BPS instanton corrections to the hyperkahler metric of the moduli space of the theory on R^3 x S^1. The wall-crossing formula reduces to th…
We present a voice conversion solution using recurrent sequence to sequence modeling for DNNs. Our solution takes advantage of recent advances in attention based modeling in the fields of Neural Machine Translation (NMT), Text-to-Speech (TTS) and Automatic Speech Recognition (ASR). The problem consists of converting be…
Bayesian tensor train kernel machine uses Laplace approximation for scalable GP regression.