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.
Numerical method for pricing exchange options with stochastic volatility and jumps.
MOL-TS uses Thompson Sampling for multi-objective linear bandits with Pareto guarantees.
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compo…
The paper explores how regularization can improve multi-objective learning with high-dimensional data.
Efficiently values and computes sensitivities of Bermudan options using Method of Lines.
New bounds show unlabeled data can significantly reduce the need for labeled data in semi-supervised multi-objective learning.
A new method for diverse Pareto solutions in multi-objective learning.
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of non-interacting fermions in 1d, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is …
Develops a framework for multi-objective learning in diffusion models with limited labeled data.
We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models a…
We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network--a composit…
AI enhances refinery optimization by detecting data errors and improving decision-making.
New model accurately predicts chemical bond breaking.
Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules wit…
Tabular in-context learners perform well on biomolecular tasks, but performance depends on the representation used.
Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this r…
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.