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Found 8 papers, 5 papers with code
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property.
Stratified-NMF for Heterogeneous Data
Non-negative matrix factorization (NMF) is an important technique for obtaining low dimensional representations of datasets.
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning.
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance.
Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities
We aim to develop a multi-modal normative modelling framework where abnormality is aggregated across variables of multiple modalities and is better able to detect deviations than uni-modal baselines.
Score-based denoising for atomic structure identification
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.
A Generalized EigenGame with Extensions to Multiview Representation Learning
We demonstrate the effectiveness of our method for solving GEPs in the stochastic setting using canonical multiview datasets and demonstrate state-of-the-art performance for optimizing Deep CCA.
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d).
