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Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common…

Machine Learning · Computer Science 2020-04-08 Gustavo A Valencia-Zapata , Daniel Mejia , Gerhard Klimeck , Michael Zentner , Okan Ersoy

We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements.…

Machine Learning · Computer Science 2023-08-15 Gregory P. Spell , Simiao Ren , Leslie M. Collins , Jordan M. Malof

We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible curve-models to each class of complex-shaped curves…

Methodology · Statistics 2013-12-30 Faicel Chamroukhi , Heré Glotin , Céline Rabouy

Network data are observed in various applications where the individual entities of the system interact with or are connected to each other, and often these interactions are defined by their associated strength or importance. Clustering is a…

Methodology · Statistics 2025-06-02 Iuliia Promskaia , Adrian O'Hagan , Michael Fop

Small-molecule foundation models are typically pretrained on standalone molecular data, unlike vision and language models that often benefit from cross-modal or relational supervision. Protein-ligand co-folding provides a molecular analogue…

Biomolecules · Quantitative Biology 2026-05-25 Hyosoon Jang , Hyunjin Seo , Honghui Kim , Seonghyun Park , Taewon Kim , Yunhui Jang , Sungsoo Ahn

Protein sequence generation for engineering requires samples that are biophysically plausible and, when targeting a family/domain, remain recognizable members while exploring within-family diversity. Current discrete generative models…

Computational Engineering, Finance, and Science · Computer Science 2026-05-25 Langzhang Liang , Ming Yang , Yi Feng , Junfan Li , Shirui Pan , Yinghui Xu , Tianlei Ying , Yizhen Zheng , Zenglin Xu

With new advances in machine learning and in particular powerful learning libraries, we illustrate some of the new possibilities they enable in terms of nonlinear system identification. For a large class of hybrid systems, we explain how…

Optimization and Control · Mathematics 2019-12-02 Mattias Fält , Pontus Giselsson

In social science research, understanding latent structures in populations through survey data with categorical responses is a common and important task. Traditional methods like Factor Analysis and Latent Class Analysis have limitations,…

Methodology · Statistics 2024-12-30 Chayut Wongkamthong

Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm fails to account for any uncertainty in…

Methodology · Statistics 2015-03-19 Ryan Martin , Surya T. Tokdar

The study of network formation is pervasive in economics, sociology, and many other fields. In this paper, we model network formation as a `choice' that is made by nodes in a network to connect to other nodes. We study these `choices' using…

Social and Information Networks · Computer Science 2022-08-30 Harsh Gupta , Mason A. Porter

Nonlinear subspace clustering based on a feed-forward neural network has been demonstrated to provide better clustering accuracy than some advanced subspace clustering algorithms. While this approach demonstrates impressive outcomes, it…

Machine Learning · Computer Science 2024-08-28 Long Shi , Lei Cao , Zhongpu Chen , Badong Chen , Yu Zhao

To gain insight into the mechanisms behind machine learning methods, it is crucial to establish connections among the features describing data points. However, these correlations often exhibit a high-dimensional and strongly nonlinear…

Machine Learning · Computer Science 2025-03-04 Lorenzo Basile , Santiago Acevedo , Luca Bortolussi , Fabio Anselmi , Alex Rodriguez

While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In the…

Machine Learning · Computer Science 2022-05-24 Zhi Wang , Chunlin Chen , Daoyi Dong

Most classification models treat all misclassifications equally. However, different classes may be related, and these hierarchical relationships must be considered in some classification problems. These problems can be addressed by using…

Machine Learning · Computer Science 2023-01-27 Hyeongji Kim , Pekka Parviainen , Terje Berge , Ketil Malde

A comparative classification scheme provides a good basis for several approaches to understand proteins, including prediction of relations between their structure and biological function. But it remains a challenge to combine a…

Biomolecules · Quantitative Biology 2015-05-20 Shuangwei Hu , Andrei Krokhotin , Antti J. Niemi , Xubiao Peng

We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity and biases. We introduce two strategies utilizing LLMs for…

Machine Learning · Computer Science 2023-11-21 Max Zhu , Siniša Stanivuk , Andrija Petrovic , Mladen Nikolic , Pietro Lio

Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of…

Biomolecules · Quantitative Biology 2024-05-10 Ian Dunn , David Ryan Koes

We introduce a regression model for data on non-linear manifolds. The model describes the relation between a set of manifold valued observations, such as shapes of anatomical objects, and Euclidean explanatory variables. The approach is…

Other Computer Science · Computer Science 2017-03-02 Line Kühnel , Stefan Sommer

Detection heterogeneity is inherent to ecological data, arising from factors such as varied terrain or weather conditions, inconsistent sampling effort, or heterogeneity of individuals themselves. Incorporating additional covariates into a…

Applications · Statistics 2021-08-27 Daniel Turek , Claudia Wehrhahn , Olivier Gimenez

Mixture models are widely used in modeling heterogeneous data populations. A standard approach of mixture modeling assumes that the mixture component takes a parametric kernel form. In many applications, making parametric assumptions on the…

Methodology · Statistics 2026-03-06 Yilei Zhang , Yun Wei , Aritra Guha , XuanLong Nguyen