260 citations · 948 across the 53 of their papers we have counts for
56 papers
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…
SSplain: Sparse and Smooth Explainer for Retinopathy of Prematurity Classification
Elifnur Sunger, Tales Imbiriba, Peter Campbell +3
Neural networks are frequently used in medical diagnosis. However, due to their black-box nature, model explainers are used to help clinicians understand better and trust model out…
Trends and Challenges in Next-Generation GNSS Interference Management
Leatile Marata, Mariona Jaramillo-Civill, Tales Imbiriba +4
The global navigation satellite system (GNSS) continues to evolve in order to meet the demands of emerging applications such as autonomous driving and smart environmental monitorin…
Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
Mariona Jaramillo-Civill, Luis González-Gudiño, Tales Imbiriba +1
Global Navigation Satellite System (GNSS) signals are vulnerable to jamming, particularly in urban areas where multipath and shadowing distort received power. Previous data-driven…
Active Jammer Localization via Acquisition-Aware Path Planning
Luis González-Gudiño, Mariona Jaramillo-Civill, Pau Closas +1
We propose an active jammer localization framework that combines Bayesian optimization with acquisition-aware path planning. Unlike passive crowdsourced methods, our approach adapt…
Hierarchical Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youth with Autism for Branching Factor Estimation
Michael Potter, Michael Everett, Deniz Erdogmus +3
Aggressive behavior in autistic inpatient youth often arises in temporally clustered bursts complicating efforts to distinguish external triggers from internal escalation. The samp…
Interpretable Augmented Physics-Based Model for Estimation and Tracking
Ondřej Straka, Jindřich Duník, Pau Closas +1
State-space estimation and tracking rely on accurate dynamical models to perform well. However, obtaining an vaccurate dynamical model for complex scenarios or adapting to changes…
Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Haoqing Li, Ricardo Borsoi, Tales Imbiriba +1
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solv…
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…
Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youths with Autism
Michael Potter, Michael Everett, Ashutosh Singh +6
Aggressive behavior, including aggression towards others and self-injury, occurs in up to 80% of children and adolescents with autism, making it a leading cause of behavioral healt…
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…
A Bayesian Framework for Clustered Federated Learning
Peng Wu, Tales Imbiriba, Pau Closas
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced d…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
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…
Mitigation of Radar Range Deception Jamming Using Random Finite Sets
Helena Calatrava, Aanjhan Ranganathan, Tales Imbiriba +3
This paper presents a radar target tracking framework for addressing main-beam range deception jamming attacks using random finite sets (RFSs). Our system handles false alarms and…
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 (…
Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
Michael Potter, Murat Akcakaya, Marius Necsoiu +3
Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the recei…
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5
We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time re…
On the Impact of Sampling on Deep Sequential State Estimation
Helena Calatrava, Ricardo Augusto Borsoi, Tales Imbiriba +1
State inference and parameter learning in sequential models can be successfully performed with approximation techniques that maximize the evidence lower bound to the marginal log-l…
Tubular Curvature Filter: Pointwise Curvature Calculation for Tubular Objects in Images
Elifnur Sunger, Beyza Kalkanli, Veysi Yildiz +4
Purpose: Accurate estimation of blood vessel tortuosity from medical images is an extremely important and challenging task. It is particularly relevant in the context of retinopath…
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
Ricardo Augusto Borsoi, Deniz Erdoğmuş, Tales Imbiriba
Although considerable effort has been dedicated to improving the solution to the hyperspectral unmixing problem, non-idealities such as complex radiation scattering and endmember v…
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger +5
Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem t…