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Multimodal recommender systems (MRS) integrate heterogeneous user and item data, such as text, images, and structured information, to enhance recommendation performance. The emergence of large language models (LLMs) introduces new…

Information Retrieval · Computer Science 2025-05-16 Alejo Lopez-Avila , Jinhua Du

Structured representations, exemplified by Abstract Meaning Representation (AMR), have long been pivotal in computational linguistics. However, their role remains ambiguous in the Large Language Models (LLMs) era. Initial attempts to…

Computation and Language · Computer Science 2025-02-21 Jiahuan Zhang , Tianheng Wang , Hanqing Wu , Ziyi Huang , Yulong Wu , Dongbai Chen , Linfeng Song , Yue Zhang , Guozheng Rao , Kaicheng Yu

Experimental neuroscience increasingly requires tractable models for analyzing and predicting the behavior of neurons and networks. The generalized linear model (GLM) is an increasingly popular statistical framework for analyzing neural…

Neural and Evolutionary Computing · Computer Science 2014-04-09 Jonathon Shlens

This paper proposes a single form for statistical models that accommodates a broad range of models, from ordinary least squares to agent-based microsimulations. The definition makes it almost trivial to define morphisms to transform and…

Methodology · Statistics 2015-02-10 Ben Klemens

Long Short-Term Memory (LSTM) neural network models have become the cornerstone for sequential data modeling in numerous applications, ranging from natural language processing to time series forecasting. Despite their success, the problem…

Machine Learning · Statistics 2026-05-26 Fahad Mostafa

Logistic regression is the most commonly used method for constructing predictive models for binary responses. One significant drawback to this approach, however, is that the asymptotes of the logistic response function are fixed at 0 and 1,…

Methodology · Statistics 2026-02-09 Anthony Almudevar , Jacob Almudevar

Functional data analysis is proved to be useful in many scientific applications. The physical process is observed as curves and often there are several curves observed due to multiple subjects, providing the replicates in statistical sense.…

Methodology · Statistics 2018-01-30 Tapabrata Maiti , Abolfazl Safikhani , Ping-Shou Zhong

Modeling & Simulation (M&S) approaches such as agent-based models hold significant potential to support decision-making activities in health, with recent examples including the adoption of vaccines, and a vast literature on healthy eating…

Artificial Intelligence · Computer Science 2025-09-08 Philippe J. Giabbanelli , Ameeta Agrawal

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms,…

We propose an L1-penalized algorithm for fitting high-dimensional generalized linear mixed models. Generalized linear mixed models (GLMMs) can be viewed as an extension of generalized linear models for clustered observations. This…

Computation · Statistics 2014-06-03 Jürg Schelldorfer , Lukas Meier , Peter Bühlmann

Researchers in the behavioral and social sciences use linear discriminant analysis (LDA) for predictions of group membership (classification) and for identifying the variables most relevant to group separation among a set of continuous…

Methodology · Statistics 2025-05-28 Ricarda Graf , Marina Zeldovich , Sarah Friedrich

Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on developing numeric coding schemes for non-numeric…

Machine Learning · Computer Science 2023-11-27 Boris Kovalerchuk , Elijah McCoy

High-dimensional linear and nonlinear models have been extensively used to identify associations between response and explanatory variables. The variable selection problem is commonly of interest in the presence of massive and complex data.…

Methodology · Statistics 2017-08-10 Vitara Pungpapong , Min Zhang , Dabao Zhang

Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress. Such methods often take the form of agentic-style iterative workflows that repeatedly…

Machine Learning · Computer Science 2026-02-23 Stefan Wahl , Raphaela Schenk , Ali Farnoud , Jakob H. Macke , Daniel Gedon

Language modeling studies the probability distributions over strings of texts. It is one of the most fundamental tasks in natural language processing (NLP). It has been widely used in text generation, speech recognition, machine…

Computation and Language · Computer Science 2024-07-18 Chengwei Wei , Yun-Cheng Wang , Bin Wang , C. -C. Jay Kuo

Across languages, numeral systems vary widely in how they construct and combine numbers. While humans consistently learn to navigate this diversity, large language models (LLMs) struggle with linguistic-mathematical puzzles involving…

Computation and Language · Computer Science 2025-10-16 Antara Raaghavi Bhattacharya , Isabel Papadimitriou , Kathryn Davidson , David Alvarez-Melis

Identifying governing equations for a dynamical system is a topic of critical interest across an array of disciplines, from mathematics to engineering to biology. Machine learning -- specifically deep learning -- techniques have shown their…

Dynamical Systems · Mathematics 2026-05-07 Nibodh Boddupalli , Timothy Matchen , Jeff Moehlis

Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the…

There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these…

Machine Learning · Statistics 2022-09-22 JM Gorriz , R. Martin-Clemente , C. G. Puntonet , A. Ortiz , J. Ramirez , J. Suckling

Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and…

Methodology · Statistics 2025-06-04 Sina Mews , Jan-Ole Koslik , Roland Langrock