6 papers
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3
We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointl…
High-Fidelity 3D Simulator for Synthetic fNIRS Data Generation
Condell Eastmond, Niels Bracher, Xavier Intes +1
Functional near-infrared spectroscopy (fNIRS) provides a noninvasive window into brain activity by measuring task-related changes in oxygenated and deoxygenated hemoglobin in the c…
Inverting Foundation Models of Brain Function with Simulation-Based Inference
Niels Bracher, Xavier Intes, Stefan T. Radev
Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks and modalities. A natural next s…
Compositional amortized inference for large-scale hierarchical Bayesian models
Jonas Arruda, Vikas Pandey, Catherine Sherry +4
Amortized Bayesian inference (ABI) with neural networks has emerged as a powerful simulation-based approach for estimating complex mechanistic models. However, extending ABI to hie…
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan +6
Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters si…
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging
Ismail Erbas, Vikas Pandey, Aporva Amarnath +4
Fluorescence lifetime imaging (FLI) is an important technique for studying cellular environments and molecular interactions, but its real-time application is limited by slow data a…