59 citations · 120 across the 7 of their papers we have counts for
7 papers
Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction
Jinghao Cao, Minsung Kang, Hongyue Sun +5
Predicting droplet evolution in material jetting, or Inkjet Printing (IJP), is essential for maintaining printing quality. However, long-horizon forecasts remain challenging due to…
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
Raghav Gnanasambandam, Bo Shen, Jihoon Chung +3
Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weath…
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
Raed Kontar, Naichen Shi, Xubo Yue +12
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is br…
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue, Maher Nouiehed, Raed Al Kontar
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique desi…
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Xubo Yue, Raed Al Kontar
Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over…
The Renyi Gaussian Process: Towards Improved Generalization
Xubo Yue, Raed Kontar
We introduce an alternative closed form lower bound on the Gaussian process () likelihood based on the Rényi -divergence. This new lower bound can be viewed as a c…
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
Xubo Yue, Raed Kontar
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a mu…