7 citations · 19 across the 10 of their papers we have counts for
10 papers
Revisiting semi-supervised training objectives for differentiable particle filters
Jiaxi Li, John-Joseph Brady, Xiongjie Chen +1
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on…
Learning Differentiable Particle Filter on the Fly
Jiaxi Li, Xiongjie Chen, Yunpeng Li
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing a…
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
Xiongjie Chen, Yunpeng Li
By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. W…
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Xiongjie Chen, Yunpeng Li, Yongxin Yang
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-worl…
Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
Xiongjie Chen, Yunpeng Li
Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods. Recent advances in differentiable particle filters have led to various eff…
Differentiable Particle Filters through Conditional Normalizing Flow
Xiongjie Chen, Hao Wen, Yunpeng Li
Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiabl…
End-To-End Semi-supervised Learning for Differentiable Particle Filters
Hao Wen, Xiongjie Chen, Georgios Papagiannis +2
Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications. The dynamic…
Imitation Learning with Sinkhorn Distances
Georgios Papagiannis, Yunpeng Li
Imitation learning algorithms have been interpreted as variants of divergence minimization problems. The ability to compare occupancy measures between experts and learners is cruci…
Augmented Sliced Wasserstein Distances
Xiongjie Chen, Yongxin Yang, Yunpeng Li
While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sli…
BCCNet: Bayesian classifier combination neural network
Olga Isupova, Yunpeng Li, Danil Kuzin +3
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (…