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.
CLARITY compares dissimilar datasets, identifying structural and relationship inconsistencies.
Policy gradient methods do not optimize the discounted objective, leading to suboptimal results.
Proposes a new noise injection method for neural networks that improves accuracy and representation clarity.
Extensive rewrite. Tables and proofs have been reformatted and/or rewritten for clarity.
Study separates learning rate effects from adaptive gradient methods.
Conference compiles problems on foliations and diffeomorphisms.
This research optimizes Andrews plots for better visual clarity in high-dimensional data.
Method improves clarity in forecasting spatio-temporal data.
GCAO improves clustering of high-dimensional data by grouping low-density boundary points.
Optimal transport aligns source and target distributions for linear regression in 2D.
New algorithm recovers communities in broader network models.
End-to-end voice conversion without vocoder.
We consider a natural Riemannian metric on the infinite dimensional manifold of all embeddings from a manifold into a Riemannian manifold, and derive its geodesic equation in the case $\Emb(\Bbb R,\Bbb R)$ which turns out to be Burgers' equation. Then we derive the geodesic equation, the curvature, and the Jacobi equat…
Lion optimizer performs well in training AI models with memory efficiency.
We provide equivalence of numerous no-free-lunch type conditions for financial markets where the asset prices are modeled as exponential Levy processes, under possible convex constraints in the use of investment strategies. The general message is the following: if any kind of free lunch exists in these models it has to…
This paper simplifies the Nash Bargaining Solution for use in intellectual property cases.
In this paper, we review or introduce several differential structures on manifolds in the general setting of real and complex differential geometry, and apply this study to Teichmüller theory. We focus on bi-Lagrangian i.e. para-Kähler structures, which consist of a symplectic form and a pair of transverse Lagrangian f…
A new framework for probabilistic learning using Maximum Probability Theorem.
The counting grid is a grid of microtopics, sparse word/feature distributions. The generative model associated with the grid does not use these microtopics individually. Rather, it groups them in overlapping rectangular windows and uses these grouped microtopics as either mixture or admixture components. This paper bui…
Study on Matérn covariance approximations on grids, finding issues with high-frequency aliasing.
Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient. From this, we devise a generic gradient boosting procedure for estimating the hazard function nonparametrically. An i…
AeGAN improves speech clarity in noisy environments.
Fidel-TS creates a new benchmark for time series forecasting models.
Local surrogate explainers vary in objectives, leading to incomparable explanations.
We summarize a book under publication with his title written by the three present authors, on the theory of Zipf's law, and more generally of power laws, driven by the mechanism of proportional growth. The preprint is available upon request from the authors. For clarity, consistence of language and conciseness, we disc…
We study a well-known estimator of the fractal index of a stochastic process. Our framework is very general and encompasses many models of interest; we show how to extend the theory of the estimator to a large class of non-Gaussian processes. Particular focus is on clarity and ease of implementation of the estimator an…
Novel analysis of neural networks using geometric algebra and convex optimization.
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
Study characterizes training and test risks for MAP regression with Gaussian priors.
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mech…
Ideal attribution mechanisms track model interactions for faithful watermarks.
Quantum circuits reveal pathways to dequantization in machine learning models.
The paper formalizes feature attribution to address inconsistent definitions and evaluate methods.
Reintroduces straight-through estimators for binary neural networks.
We propose an efficient algorithm for the generalized sparse coding (SC) inference problem. The proposed framework applies to both the single dictionary setting, where each data point is represented as a sparse combination of the columns of one dictionary matrix, as well as the multiple dictionary setting as given in m…
Moment Pooling reduces latent space dimensions in machine learning models.
Professional-grade software applications are powerful but complicatedexpert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like "clarity", "temp", and "high…
We consider braids with repeating patterns inside arbitrary knots which provides a multi-parametric family of knots, depending on the "evolution" parameter, which controls the number of repetitions. The dependence of knot (super)polynomials on such evolution parameters is very easy to find. We apply this evolution meth…
This study improves mid-cap equity performance with a data-driven, market-neutral approach.
These lectures were a part of the geometry course held during the Fall 2011 Mathematics Advanced Study Semesters (MASS) Program at Penn State (\url{http://www.math.psu.edu/mass/}). The lectures are meant to be accessible to advanced undergraduate and early graduate students in mathematics. We have placed a great emphas…
Improved 3D ECG feature attributions for clinical interpretation.
Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. …
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply "autodiff", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of…
We define transit clusters to simplify causal diagrams and preserve their essential properties.
Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based alg…
Study evaluates interpretability of time series foundation models' latent spaces.
Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations,…
The principle of peer review is central to the evaluation of research, by ensuring that only high-quality items are funded or published. But peer review has also received criticism, as the selection of reviewers may introduce biases in the system. In 2014, the organizers of the ``Neural Information Processing Systems\r…