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.
Investigates neural TTS systems for Japanese and English.
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…
Introduces TT-NF for more compact neural field representations.
TensorGuide improves LoRA efficiency and expressivity through joint tensor-train optimization.
CNPs improve function approximation by contrastive learning.
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…
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…
When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead. Many studies have shown that neural multi-speaker TTS model trained with a small amount data f…
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.
Relates quantum cohomology to tt*-Toda equations for minuscule flag manifolds.
Analyzes tt*-structures from -type Stokes data.
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.
Study of symplectic groupoids from tt*-Toda equations.
Solutions of tt*-equation from SU(2)_k fusion algebra.
We present a meta-learning approach for adaptive text-to-speech (TTS) with few data. During training, we learn a multi-speaker model using a shared conditional WaveNet core and independent learned embeddings for each speaker. The aim of training is not to produce a neural network with fixed weights, which is then deplo…
Current multi-reference style transfer models for Text-to-Speech (TTS) perform sub-optimally on disjoints datasets, where one dataset contains only a single style class for one of the style dimensions. These models generally fail to produce style transfer for the dimension that is underrepresented in the dataset. In th…
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.
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
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…
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.
Characterizes kernel of linearization for minimal surfaces problem
Tensor network surrogate for efficient option pricing in large portfolios.
New method uses TT approximations to solve HJB equations for efficient sampling.
A new method computes Greeks for multi-asset options using tensor trains and Fourier transforms.
Establishes correspondence between Calabi-Yau and Landau-Ginzburg structures.
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.
Bayesian tensor train kernel machine uses Laplace approximation for scalable GP regression.
The paper studies automorphisms of free groups with a North-South dynamics.
Paper tackles multivariate shape-constrained convex regression problems.
In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Proce…
Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights ({\tt k-means++}) has recently been used to improve the performance over this strategy in cost and run-time. We consider the k-means problem with semi-supervised inf…