3 papers
cs.AR2026
Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Zehuan Zhang, Mark Chen, He Li +1
Complex-Valued Neural Networks (CVNNs) have significant advantages in handling tasks that involve complex numbers. However, existing CVNNs are unable to quantify predictive uncerta…
cs.LG2024
Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA
Zehuan Zhang, Hongxiang Fan, Hao Mark Chen +2
The increasing deployment of artificial intelligence (AI) for critical decision-making amplifies the necessity for trustworthy AI, where uncertainty estimation plays a pivotal role…
cs.LG2023
When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA
Hongxiang Fan, Hao Chen, Liam Castelli +4
Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous dr…