arXiv Machine Learning

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data

arXiv:2608. 05930v1 Announce Type: cross Abstract: The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multiple times a day.

arXiv Machine Learning

Jul 7

Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data

arXiv:2607. 04647v1 Announce Type: cross Abstract: Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predictors and do not directly accommodate high-dimensional modalities such as images and text.

By Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros, Samuel Berchuck, Matthew M. Engelhard

multimodal benchmarks

arXiv Machine Learning

Jul 10

CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation

arXiv:2607. 08254v1 Announce Type: new Abstract: Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.

By Sai Spandana Chintapalli, Pratik Chaudhari, Christos Davatzikos

arXiv AI

Jun 2

Towards a General Intelligence and Interface for Wearable Health Data

arXiv:2605. 22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging.

By Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari, Samy Abdel-Ghaffar, Marius Guerard, Kara Vaillancourt, Zhihan Zhang, Jake Garrison, Levi Albuquerque, Dimitris Spathis, Hong Yu, Hamid Palangi, Xuhai "Orson" Xu, David G. T. Barrett, Joseph Breda, Jed McGiffin, Yubin Kim, Yuwei Zhang, Naghmeh Rezaei, Samuel Solomon, Karan Ahuja, Tim Althoff, Jake Sunshine, Ming-Zher Poh, Benjamin Yetton, Ari Winbush, Nicholas B. Allen, James M. Rehg, Isaac Galatzer-Levy, Yun Liu, John Hernandez, Anupam Pathak, Conor Heneghan, Yuzhe Yang, Ahmed A. Metwally, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Xin Liu, Daniel McDuff

llms rag agents

arXiv Machine Learning

Jun 30

Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data

arXiv:2511. 00217v2 Announce Type: replace-cross Abstract: We introduce Gradient Boosted Mixed Models (GBMixed), a framework which extends boosting to clustered data by jointly modeling the mean and variance components in a linear mixed model via likelihood-based gradients.

By Mitchell L. Prevett, Francis K. C. Hui, Zhi Yang Tho, A. H. Welsh, Anton H. Westveld

arXiv Machine Learning

Jun 15

Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

arXiv:2602. 12379v2 Announce Type: replace Abstract: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback.

By Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

llms benchmarks safety

arXiv Machine Learning

Jun 25

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.

By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 9

Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework

arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.

By Lemei Zhang, Peng Liu, Hans Dahle Kvadsheim, August S{\ae}tre Aasv{\ae}r, Shuer Ye, Reza Bonyadi, Maryam Ziaei, Jon Atle Gulla

llms

arXiv Machine Learning

Jul 7

Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition

arXiv:2607. 04139v1 Announce Type: new Abstract: Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization.

By Huqin Weng, Jiayang Huang, Yimin Wen, Jie Du, Chi-Man Vong, Chuangquan Chen

rag fine-tuning

arXiv AI

Jun 6

EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction

arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.

By Zhihao Zhou, Weishan Ye, Li Zhang, Gan Huang, Zhen Liang

llms diffusion reinforcement-learning efficiency benchmarks

arXiv AI

Jun 11

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

arXiv:2506. 20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer analyses to miss this cross-layer structure.

By Ankur Garg, Xuemin Yu, Hassan Sajjad, Samira Ebrahimi Kahou

llms diffusion efficiency

arXiv Machine Learning

Jun 25

Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning

arXiv:2606. 25770v1 Announce Type: new Abstract: Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities.

By Muhammed Furkan Dasdelen, Fatih Ozlugedik, Anastasia Litinetskaya, Nassir Navab, Carsten Marr, Ario Sadafi

rag safety

arXiv AI

Jul 14

Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning

arXiv:2607. 10678v1 Announce Type: new Abstract: Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states.

By Qing Lin, Mengmi Zhang

fine-tuning multimodal benchmarks

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