arXiv research

DeepDTA predicts drug-target binding affinities using deep learning.

DeepONet accelerates nuclear DT inference with high accuracy and efficiency.

Decision tree predicts DV recidivism with interpretable models.

This study examines hyperparameter tuning for CART and C4.5 DT algorithms.

Proposes DTS framework to predict CTR by tracking user interest evolution over time.

Study DT invariants on Calabi-Yau 4-folds, linking to 3-folds.

Let $X$ be a compact complex Calabi-Yau 4-fold. Under certain assumptions, we define Donaldson-Thomas type deformation invariants ($DT_{4}$ invariants) by studying moduli spaces of solutions to the Donaldson-Thomas equations on $X$. We also study sheaves counting problems on local Calabi-Yau 4-folds. We relate $DT_{4}$…

The article finds non-Abelian DT-instantons on non-Kähler manifolds.

Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.

Enhances RL in target domains with limited data using augmented return.

New method builds hyperbolic spheres with controlled holonomy.

Enhanced VAE with DT improves flexibility in latent variable modeling.

Study evaluates digital transformation impact on financial performance using LLMs.

This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.

Study uses AI techniques to predict bank customer solvency.

Mathematical advances needed for Digital Twins, differing from traditional models.

Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their dec…

Boosted DT classifiers become DP with new calibrated loss.

We consider differential operators on a supermanifold of dimension $1|1$. We define non-degenerate operators as those with an invertible top coefficient in the expansion in the "superderivative" $D$ (which is the square root of the shift generator, the partial derivative in an even variable, with the help of an odd ind…

The paper constructs Darboux transforms for a specific hierarchy and its related flows.

Paper proposes a new method to secure power system operation using machine learning.

GDT improves reinforcement learning by matching future state information efficiently.

DTS improves inference-time alignment of diffusion models with less compute.

A new Bayesian model improves dynamic texture segmentation.

Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.

Growth of monetary assets and debts is commonly described by the formula of compound interest which for the case of continuous compounding is the exponential growth law. Its differential form is dc/dt = i c where dc/dt describes the rate of monetary growth, i the compounded interest rate and c the actual principal. Exp…

New rule reduces exploration regret to logarithmic, improving bad episode handling.

Paper proposes DG-ETC for online submodular maximization with stochastic bandit feedback.

DTS improves robustness of bandit algorithms in nonstationary environments.

For a given time horizon DT, this article explores the relationship between the realized volatility (the volatility that will occur between t and t+DT), the implied volatility (corresponding to at-the-money option with expiry at t+DT), and several forecasts for the volatility build from multi-scales linear ARCH process…

Researchers use DT to transfer policies from one environment to another using causal reasoning.

Transform ANNs into interpretable decision trees.

Paper defines new risk measures for elliptical distributions.

Study computes isoperimetric profiles in low-dimensional Riemannian products.

Mathematical structures link Gromov-Witten to Donaldson-Thomas invariants.

Records of the traded value f_i(t) of stocks display fluctuation scaling, a proportionality between the standard deviation sigma(i) and the average <f(i)>: sigma(i) ~ f(i)^alpha, with a strong time scale dependence alpha(dt). The non-trivial (i.e., neither 0.5 nor 1) value of alpha may have different origins and provid…

Proposes DT-LET for better transfer learning between domains with different resolutions.

Study biharmonic submanifolds in warped product structures.

In this paper we propose a synergistic melting of neural networks and decision trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where each splitting node is an independent multilayer perceptron allowing oblique decision functions or arbritrary nonlinear decision function if more…

Proof of wall-crossing formula using spectral networks.

The study examines the properties of mapping class groups under specific Dehn twist subgroups.

We present an empirical study of the subordination hypothesis for a stochastic time series of a stock price. The fluctuating rate of trading is identified with the stochastic variance of the stock price, as in the continuous-time random walk (CTRW) framework. The probability distribution of the stock price changes (log…

Random Forests are reinterpreted as generative models to handle missing data and detect outliers.

We develop DTs for PDE models using KL-NN and TL, analyzing TL's moment equations and one-shot learning for exactness.

GANs generate training data for machine learning tasks.

Neural networks estimate SDEs with jump noise using a Tamed-Milstein scheme.

We review a construction of hyperkahler metrics proposed in joint work of Davide Gaiotto, Greg Moore and the author. A key ingredient in this construction is a collection of integer "DT invariants" obeying the wall-crossing formula of Kontsevich-Soibelman.

Study online pricing with contextual elasticity and heteroscedastic valuation.