Weizhong Wang

Haojian Huang, Zhe Liu, Sukumar Letchmunan et al.

Incomplete multi-view data classification poses significant challenges due to the common issue of missing views in real-world scenarios. Despite advancements, existing methods often fail to provide reliable predictions, largely due to the uncertainty of missing views and the inconsistent quality of imputed data. To tackle these problems, we propose a novel framework called Evidential Deep Partial Multi-View Classification (EDP-MVC). Initially, we use K-means imputation to address missing views, creating a complete set of multi-view data. However, the potential conflicts and uncertainties within this imputed data can affect the reliability of downstream inferences. To manage this, we introduce a Conflict-Aware Evidential Fusion Network (CAEFN), which dynamically adjusts based on the reliability of the evidence, ensuring trustworthy discount fusion and producing reliable inference outcomes. Comprehensive experiments on various benchmark datasets reveal EDP-MVC not only matches but often surpasses the performance of state-of-the-art methods.

Chenyang Wang, Weizhong Wang, Yinuo Ren et al.

iffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve sample quality on task-specific objectives. However, most existing techniques require repeated score or gradient evaluations, introducing bias, high computational overhead, or both. We introduce \texttt{URGE}, Unbiased Resampling via Girsanov Estimation, a derivative-free inference-time scaling algorithm that performs path-wise importance reweighting via a Girsanov change of measure. Instead of computing gradient-based particle weights in previous work, \texttt{URGE} attaches a simple multiplicative weight to each simulated trajectory and periodically resamples. No score, no Hessian, and no PDE evaluation is required. We establish an equivalence between path-wise and particle-wise SMC: the Girsanov path weight admits a backward conditional expectation that recovers the previous particle-level weights, guaranteeing that both schemes produce the same unbiased terminal law. Empirically, \texttt{URGE} outperforms existing inference-time guidance baselines on synthetic tests and diffusion-model benchmarks, achieving better generation quality, while being significantly simpler to implement and fully gradient-free.

Weizhong Wang, Nicholas W. Brady, Chenyao Liao et al.

This paper proposes a way to augment the existing machine learning algorithm applied to state-of-charge estimation by introducing a form of pulse injection to the running battery cells. It is believed that the information contained in the pulse responses can be interpreted by a machine learning algorithm whereas other techniques are difficult to decode due to the nonlinearity. The sensitivity analysis of the amplitude of the current pulse is given through simulation, allowing the researchers to select the appropriate current level with respect to the desired accuracy improvement. A multi-layer feedforward neural networks is trained to acquire the nonlinear relationship between the pulse train and the ground-truth SoC. The experimental data is trained and the results are shown to be promising with less than 2\% SoC estimation error using layer sizes in the range of 10 - 10,000 trained in 0 - 1 million epochs. The testing procedure specifically designed for the proposed technique is explained and provided. The implementation of the proposed strategy is also discussed. The detailed system layout to perform the augmented SoC estimation integrated in the existing active balancing hardware has also been given.