7 citations · 10 across the 7 of their papers we have counts for
7 papers
STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation
Mena Kamel, Natalie Won, Amrut Sarangi +2
Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-view methods provide superior…
Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records
Jun Ni Du, Lukas Adamek, Maxim Kryukov +4
Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory inform…
Benders Decomposition for Passenger-Oriented Train Timetabling with Hybrid Periodicity
Zhiyuan Yao, Anita Schöbel, Lei Nie +1
Periodic timetables are widely adopted in passenger railway operations due to their regular service patterns and well-coordinated train connections. However, fluctuations in passen…
Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction
Jonathan Broadbent, Michael Bailey, Mingxuan Li +6
Accurate prediction of small molecule solubility using material-sparing approaches is critical for accelerating synthesis and process optimization, yet experimental measurement is…
Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions
Roshan Patel, Saeed Moayedpour, Louis De Lescure +4
Machine learning and Bayesian optimization (BO) algorithms can significantly accelerate the optimization of chemical reactions. Transfer learning can bolster the effectiveness of B…
Many-Shot In-Context Learning for Molecular Inverse Design
Saeed Moayedpour, Alejandro Corrochano-Navarro, Faryad Sahneh +14
Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) for a variety of generative and discriminative chemical design tasks. The new…
An Improved Greedy Algorithm for Stochastic Online Scheduling on Unrelated Machines
Sven Jäger
Most practical scheduling applications involve some uncertainty about the arriving times and lengths of the jobs. Stochastic online scheduling is a well-established model capturing…