13 citations · 33 across the 21 of their papers we have counts for
7 papers · 1 filter
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning
Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen +9
Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text rather than as temporal evidence…
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck +16
Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processi…
How Class Ontology and Data Scale Affect Audio Transfer Learning
Manuel Milling, Andreas Triantafyllopoulos, Alexander Gebhard +2
Transfer learning is a crucial concept within deep learning that allows artificial neural networks to benefit from a large pre-training data basis when confronted with a task of li…
Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning
Simon Rampp, Manuel Milling, Andreas Triantafyllopoulos +1
Curriculum learning (CL) describes a machine learning training strategy in which samples are gradually introduced into the training process based on their difficulty. Despite a par…
Depression Diagnosis and Forecast based on Mobile Phone Sensor Data
Xiangheng He, Andreas Triantafyllopoulos, Alexander Kathan +9
Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires…
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He +9
Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A comm…
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations
Andreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos +1
Underspecification and fairness in machine learning (ML) applications have recently become two prominent issues in the ML community. Acoustic scene classification (ASC) application…