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Found 30 papers, 13 papers with code
PaPaGei: Open Foundation Models for Optical Physiological Signals
PaPaGei is more data- and parameter-efficient than other foundation models or methods, as it outperforms 70x larger models.
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
However, existing CL methods often introduce false negative pairs (FNPs) by neglecting inherent characteristics and then randomly selecting distinct segments as dissimilar pairs, leading to erroneous representation learning, reduced model performance, and overall inefficiency.
Using Self-supervised Learning Can Improve Model Fairness
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels.
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.
Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement
We show that FHLR achieves significantly better performance when learning from noisy labels and achieves state-of-the-art by a large margin, with up to 19% accuracy improvement under symmetric and asymmetric noise.
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning.
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels.
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights.
The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models
Here, we discuss recent works that employ LLMs for human-centric tasks such as in mobile health sensing and present a case study showing that popular LLMs tokenize temporal data incorrectly.
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field.
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-consuming.
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
The field of mobile and wearable computing is undergoing a revolutionary integration of machine learning.
Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction
Deep learning models have shown great promise in various healthcare applications.
Looking for Out-of-Distribution Environments in Multi-center Critical Care Data
Clinical machine learning models show a significant performance drop when tested in settings not seen during training.
Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality.
A Summary of the ComParE COVID-19 Challenges
The COVID-19 pandemic has caused massive humanitarian and economic damage.
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes
Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical.
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables
Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated.
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition.
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring.
Self-supervised transfer learning of physiological representations from free-living wearable data
Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level.
Learning Generalizable Physiological Representations from Large-scale Wearable Data
To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
Interactive dimensionality reduction using similarity projections
In order to visualize that data in 2D or 3D, usually Dimensionality Reduction (DR) techniques are employed.
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words
Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings.
Photo-Quality Evaluation based on Computational Aesthetics: Review of Feature Extraction Techniques
Researchers try to model the aesthetic quality of photographs into low and high- level features, drawing inspiration from art theory, psychology and marketing.
