Search · four archives
Search · four archives
17 papers · ranked by Valyu relevance
Andrew Coristine, Grzegorz Bulaj, Beibin Li, Weilin Xu + 1 more
'Peter Washington'] Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of disease exacerbations, enabling timely interventions and…
Lili Blumenberg, Kelly V. Ruggles
Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models…
Zexi Li, Lingzhi Gao, Dongqi Cai, Nicholas D. Lane + 1 more
Title: Summary Generative artificial intelligence (GenAI) has advanced rapidly across modalities, from text-to-text large language models to text-to-image and text-to-video diffusion models. Here, we investigate text-to-model generation: whether GenAI can map semantic task descriptions to functional neural network…
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi + 2 more
'Xing Xie'] Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the…
Nat Roth, Justin Wagle
We introduce a new metric for measuring how well a model personalizes to a user's specific preferences. We define personalization as a weighting between performance on user specific data and performance on a more general global dataset that represents many different users. This global term serves as a form of…
Nathan Kallus
We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The…
Harri Oinas-Kukkonen, Sami Pohjolainen, Eunice Agyei
A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for…
Travis Greene, Galit Shmueli
Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of…
Saber Kazeminasab, Sayuri Sekimitsu, Mojtaba Fazli, Mohammad Eslami + 6 more
Artificial intelligence (AI) has been increasingly used to analyze optical coherence tomography (OCT) images to better understand physiology and genetic architecture of ophthalmic diseases. However, to date, research has been limited by the inability to transfer OCT phenotypes from one dataset to another. In this work…
Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng + 7 more
Models for Generative Recommendation and Beyond Authors: ['Tianxin Wei' 'Bowen Jin' 'Ruirui Li' 'Hansi Zeng' 'Zhengyang Wang' 'Jianhui Sun' 'Qingyu Yin' 'Hanqing Lu' 'Suhang Wang' 'Jingrui He' 'Xianfeng Tang'] Developing a unified model that can effectively harness heterogeneous resources and respond to a wide range of…
Suruchi Jai Kumar Ahuja
A major objective of clustering is to identify groups in the data that maximizes the similarity between objects within the same cluster and minimizes the similarity between different clusters. A challenge for data clustering, and unsupervised learning in general, is that there is often no mechanism for feature…
Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of…
Jialin Li, Alia Waleed, Hanan Salam
—In computing, the aim of personalization is to train a model that caters to a specific individual or group of people by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective and personality computing (hereinafter referred to…
Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede
In real-world applications, teams often develop separate models to solve search and recommendation tasks. Throughout various services, it is common to have query-driven item searches, item-toitem similarity-based recommendations as well as other kinds of more traditional recommendations. It is often the case that teams…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Stephan Spiegel, Imtiaz Hossain, Christopher Ball, Xian Zhang
The clustering of biomedical images according to their phenotype is an important step in early drug discovery. Modern high-content-screening devices easily produce thousands of cell images, but the resulting data is usually unlabelled and it requires extra effort to construct a visual representation that supports the…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…