15 citations · 29 across the 16 of their papers we have counts for
8 papers · 1 filter
RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba +2
Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the…
Exploring Theory-Laden Observations in the Brain Basis of Emotional Experience
Christiana Westlin, Ashutosh Singh, Deniz Erdogmus +2
In the science of emotion, it is widely assumed that folk emotion categories form a biological and psychological typology, and studies are routinely designed and analyzed to identi…
Recursive Deep Inverse Reinforcement Learning
Paul Ghanem, Owen Howell, Michael Potter +4
Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems in domains like cybersecurity, military, and strategy…
Dependency-aware Maximum Likelihood Estimation for Active Learning
Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis +2
Active learning aims to efficiently build a labeled training set by strategically selecting samples to query labels from annotators. In this sequential process, each sample acquisi…
MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems
Elifnur Sunger, Yunus Bicer, Deniz Erdogmus +1
Brain-Computer Interfaces (BCIs) help people with severe speech and motor disabilities communicate and interact with their environment using neural activity. This work focuses on t…
Learning Physics Informed Neural ODEs With Partial Measurements
Paul Ghanem, Ahmet Demirkaya, Tales Imbiriba +3
Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the…
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
Ashutosh Singh, Ashish Singh, Tales Imbiriba +2
Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are…
Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
Ashutosh Singh, Ricardo Augusto Borsoi, Deniz Erdogmus +1
Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (…