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.
The paper shows how neural networks with less decision boundary variability generalize better.
problem Improving neural network generalizability by reducing decision boundary variability.
method Introduces new measures (algorithm DB variability and (ε,η)-data DB variability) to quantify decision boundary variability and proves theoretical bounds on generalizability.
result Neural networks with lower decision boundary variability have better generalizability, as shown by extensive experiments and theoretical bounds.
Deep brain stimulation (DBS) is a surgical treatment for Parkinson's Disease. Static models based on quasi-static approximation are common approaches for DBS modeling. While this simplification has been validated for bioelectric sources, its application to rapid stimulation pulses, which contain more high-frequency pow…
Let (g,b,J_+,J_-) be the bihermitian structure corresponding to a generalized Kaehler structure. We find natural integrability conditions under which db=0.
Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system…
We investigate the behavior of the higher-order degrees, db_n, of a finitely presented group G. These db_n are functions from H^1(G;Z) to Z whose values are the degrees certain higher-order Alexander polynomials. We show that if def(G) is at least 1 or G is the fundamental group of a compact, orientable 3-manifold then…
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
This paper tackles combinatorial pure exploration for dueling bandits, aiming to find the best candidate-position match.
problem Finding the best candidate-position match in a dueling bandit setting.
method The paper adapts combinatorial pure exploration for multi-armed bandits to dueling bandits, considering both Borda winner and Condorcet winner cases. It designs PAC and exact algorithms for Borda winner and a fully polynomial time approximation scheme (FPTAS) for Condorcet winner.
result The paper introduces the first algorithm with polynomial running time per round for identifying the Condorcet winner in CPE-DB.
We investigate a new 8-dimensional Riemannian geometry defined by a generic closed and coclosed 3-form with stabiliser PSU(3), and which arises as a critical point of Hitchin's variational principle. We give a Riemannian characterisation of this structure in terms of invariant spinor-valued 1-forms, which are harmonic …
Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified…
Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication performance in impulsive noise. Alternate designs assume a parametr…
Instead of requiring a domain expert to specify the probabilistic dependencies of the data, in this work we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for columns, latent variables th…
A machine learning model for PMD compensation in dual-polarization systems.
problem Compensating for polarization-mode dispersion (PMD) in dual-polarization systems.
method Model-based machine learning approach using the split-step Fourier method for the Manakov-PMD equation.
result The model converges to within 1% of peak dB performance after 428 iterations, achieving a 0.30 dB reduction in effective signal-to-noise ratio compared to PMD-free case.
Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning, such as support vector machine (SVM) and kernel principal component analysis (kernel PCA). Kernel trick is to define a kernel function which relies on the inner-…
Using the large deviation principle (LDP) for a re-scaled fractional Brownian motion BtH where the rate function is defined via the reproducing kernel Hilbert space, we compute small-time asymptotics for a correlated fractional stochastic volatility model of the form $dS_t=S_tσ(Y_t) (\barρ dW_t +ρdB_t), \,dY_t=dB^H…
The one-dimensional SDE with non Lipschitz diffusion coefficient dXt=b(Xt)dt+σXtγdBt,X0=x,γ<1 is widely studied in mathematical finance. Several works have proposed asymptotic analysis of densities and implied volatilities in models involving instances of this equation, based on a careful i…
Deep learning models improve sound separation across various types of sounds.
problem Developing a universal method to separate arbitrary sounds of different types.
method Created a dataset of mixtures containing arbitrary sounds, investigated mask-based separation architectures, and tested different framewise analysis-synthesis bases.
result STFT outperformed learnable bases in universal sound separation tasks.
A class of groups is investigated, each of which has a fairly simple presentation . For example the group R=(a,b,c,d∣a3=b3=c3=d3=1,ba−1=dc−1,ca−1=db−1) is in the class. Such a group does not have as a homomorphic image any group which is a 2-orbifold group or which is a group of i…