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.
ANN model predicts zinc leaching filter cake moisture accurately.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
A new neural network model for molecular graphs that learns efficiently and accurately.
Discrete structure rules for validating molecular structures are usually limited to fulfillment of the octet rule or similar simple deterministic heuristics. We propose a model, inspired by language modeling from natural language processing, with the ability to learn from a collection of undirected molecular graphs, en…
Advanced GNNs improve molecular generation models.
This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distribu…
Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previous work, we encode molecules into continuous vectors in the latent space and then decode the vectors into molecules under the variational autoencoder (VAE) framework.…
A new unpooling layer enhances graph generation in molecular models.
A new HOM model improves forecasting of Indian base metal prices.
MFNs parameterize non-local interactions through matrix equivariant functions, improving graph neural network performance.
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
Molecule optimization is about generating molecule with more desirable properties based on an input molecule . The state-of-the-art approaches partition the molecules into a large set of substructures and grow the new molecule structure by iteratively predicting which substructure from to add. However, s…
Enhances graph neural networks with structural message-passing for better generalization.
We propose a simple auto-encoder framework for molecule generation. The molecular graph is first encoded into a continuous latent representation , which is then decoded back to a molecule. The encoding process is easy, but the decoding process remains challenging. In this work, we introduce a simple two-step decodin…
A Graph Neural Network model for generating molecular graphs.
Benchmarking graph neural networks for diverse datasets.
Enhances GNNs to better capture local graph structures.
New framework extends graph neural networks by improving expressiveness and space efficiency.