Search · four archives
Search · four archives
20 papers · ranked by Valyu relevance
Muskan Gupta, Suraj Thapa, Jyotsana Khatri
Session-based recommendation systems (SBRS) aim to capture user's short-term intent from interaction sequences. However, the common assumption of anonymous sessions limits personalization, particularly under sparse or cold-start conditions. Recent advances in LLM-augmented recommendation have shown that LLMs can…
Maxence Lapatrie, Jason da Silva Castanheira, Idil Aydin, Sylvain Baillet
Human brain activity contains stable, individual-specific features that persist over months to years, forming neurophysiological profiles. Most model-based profiling approaches use participant labels or supervised objectives, making it difficult to determine whether successful differentiation reflects stable biology or…
Manuela González-González, Soufiane Belharbi, Muhammad Osama Zeeshan, Masoumeh Sharafi + 7 more
Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes. In-person interventions are costly and difficult to scale, especially in resource-limited regions. Digital health interventions offer a…
Rameen Abdal, James Burgess, Sergey Tulyakov, Kuan-Chieh Wang
We introduce the Visual Personalization Turing Test (VPTT), a new paradigm for evaluating contextual visual personalization based on perceptual indistinguishability, rather than identity replication. A model passes the VPTT if its output (image, video, 3D asset, etc.) is indistinguishable to a human or calibrated VLM…
Ozan Oguztuzun, Çerağ Oğuztüzün
Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that…
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…
Heeyoon Yoon, Min-Gyu Kim, SunKyoung Kim, Jin-Ho Suh + 1 more
Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling…
Zhaoqi Li, Emma Brunskill
From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's potential benefits with its possible increased cost and fragility. We introduce a statistical hypothesis test that evaluates, given…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Mo Zhou, Emily Schwartz, Arish Alreja, R. Mark Richardson + 2 more
Deep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Yifan Miao, Weishan Zhang, Yuhan Wang, Yuru Liu + 3 more
Personalized federated learning (pFL) aims to address data heterogeneity by training client-specific models. However, it faces two critical challenges under few-shot conditions. First, existing methods often overlook the hierarchical structure of neural representations, limiting their ability to balance generalization…
Stefano Canali, Alessandro Falcetta, Massimo Pavan, Manuel Roveri + 1 more
The use of big data and machine learning has been discussed in an expanding literature, detailing concerns on ethical issues and societal implications. In this paper we focus on big data and machine learning in the context of health systems and with the specific purpose of personalization. Whilst personalization is…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Dongshan Lin, Zhenyue Wang, Jiaqi Liao, Nan Li + 2 more
The growing diversity of anthropogenic chemicals in the environment far exceeds the scope of routine analytical monitoring. Non-target screening (NTS) using high-resolution mass spectrometry (HRMS) has thus emerged to discover unknown organic contaminants. Liquid or gas chromatography (LC/GC) coupled with ion…
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…
Kyohei Kinoshita, Tetsuya J. Kobayashi
Identifying antigen-specific T cell receptors (TCRs) within the diverse human repertoire remains challenging due to their extremely low frequencies, often as rare as one per million cells. Here, we propose a novel unsupervised approach that detects low-frequency antigen-specific TCRs through distance-based anomaly…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
Melina Estela, Raimo A. Salo, Isabel San Martín Molina, Omar Narvaez + 3 more
Quantitative assessment of brain histology is often constrained by predefined feature sets and labor-intensive manual annotations. To overcome these limitations, we employed unsupervised deep learning to automatically extract and quantify tissue organizational patterns from myelin-stained rat brain sections without the…
Xinming Tu, Anna Spiro, Maria Chikina, Sara Mostafavi
Sequence-to-function (S2F) models trained on reference genomes have achieved strong performance on regulatory prediction and variant-effect benchmarks, yet they still struggle to predict inter-individual variation in gene expression from personal genomes. We evaluated AlphaGenome on personal genome prediction in two…