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Linwei Wang

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9 papers

2 author rows

JBHI Journal 2025 Journal Article

A Novel 3D Camera-Based ECG-Imaging System for Electrode Position Discovery and Heart-Torso Registration

Nikhil Shenoy
Maryam Toloubidokhti
Omar Gharbia
MirMilad P. Khoshknab
Saman Nazarian
John L. Sapp
Vivek Singh
Ankur Kapoor

ECG-imaging has been receiving increasing clinical and commercialization interest as a non-invasive technology for computing cardiac electrical activity and facilitating clinical management of heart rhythm disorders. Despite its potential, the standard ECG-imaging pipeline requires thorax imaging for constructing patient-specific heart-thorax geometry – being outside standard-of-care clinical workflow, this component constitutes a major barrier to the clinical adoption of ECG-imaging. The advent of 3D cameras into ECG-imaging workflow to replace thorax imaging has shown promise, although existing works largely neglect the registration between camera-derived thorax models with an individual's heart geometry. In this work, we address this gap with a novel system that generates patient-specific torso geometry using a 3D camera, registers the torso to heart geometry obtained from pre-existing cardiac scans, and optimally deforms the torso to the skin surface of the patient. We evaluated the presented camera-based ECG-imaging system on five patients undergoing ablation of scar-related ventricular tachycardia, where we evaluate both the accuracy of the surface electrode localization and the ECG-imaging solutions in comparison to those obtained from computed tomography based thorax imaging. We further evaluate the use of the presented camera-based patient-specific heart-thorax model versus a generic heart-thorax model, highlighting the importance of the registration between the camera-based thorax models with cardiac scans.

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ICLR Conference 2025 Conference Paper

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt

Ryan Missel
Linwei Wang

An increasing interest in learning to forecast for time-series of high-dimensional observations is the ability to adapt to systems with diverse underlying dynamics. Access to observations that define a stationary distribution of these systems is often unattainable, as the underlying dynamics may change over time. Naively training or retraining models at each shift may lead to catastrophic forgetting about previously-seen systems. We present a new continual meta-learning (CML) framework to realize continual slow-and fast adaptation of latent dynamics (CoSFan). We leverage a feed-forward meta-model to infer *what* the current system is and *how* to adapt a latent dynamics function to it, enabling *fast adaptation* to specific dynamics. We then develop novel strategies to automatically detect *when* a shift of data distribution occurs, with which to identify its underlying dynamics and its relation with previously-seen dynamics. In combination with fixed-memory experience replay mechanisms, this enables continual *slow update* of the *what-how* meta-model. Empirical studies demonstrated that both the meta- and continual-learning component was critical for learning to forecast across non-stationary distributions of diverse dynamics systems, and the feed-forward meta-model combined with task-aware/-relational continual learning strategies significantly outperformed existing CML alternatives.

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ICLR Conference 2024 Conference Paper

DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks

Maryam Toloubidokhti
Yubo Ye
Ryan Missel
Xiajun Jiang
Nilesh Kumar
Ruby Shrestha
Linwei Wang

Advancements in deep learning have led to the development of physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) without being supervised by PDE solutions. While vanilla PINNs require training one network per PDE configuration, recent works have showed the potential to meta-learn PINNs across a range of PDE configurations. It is however known that PINN training is associated with different levels of difficulty, depending on the underlying PDE configurations or the number of residual sampling points available. Existing meta-learning approaches, however, treat all PINN tasks equally. We address this gap by introducing a novel difficulty-aware task sampler (DATS) for meta-learning of PINNs. We derive an optimal analytical solution to optimize the probability for sampling individual PINN tasks in order to minimize their validation loss across tasks. We further present two alternative strategies to utilize this sampling probability to either adaptively weigh PINN tasks, or dynamically allocate optimal residual points across tasks. We evaluated DATS against uniform and self-paced task-sampling baselines on two representative meta-PINN models, across four benchmark PDEs as well as three different residual point sampling strategies. The results demonstrated that DATS was able to improve the accuracy of meta-learned PINN solutions when reducing performance disparity across PDE configurations, at only a fraction of residual sampling budgets required by its baselines.

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NeurIPS Conference 2024 Conference Paper

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution

Yubo Ye
Maryam Toloubidokhti
Sumeet Vadhavkar
Xiajun Jiang
Huafeng Liu
Linwei Wang

The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of hybrid deep generative models (hybrid-DGMs) that integrates known physics-based mathematical expressions in neural generative models. To identify these hybrid-DGMs requires inferring parameters of the physics-based component along with their neural component. The identifiability of these hybrid-DGMs, however, has not yet been theoretically probed or established. How does the existing theory of the un-identifiability of general DGMs apply to hybrid-DGMs? What may be an effective approach to consutrct a hybrid-DGM with theoretically-proven identifiability? This paper provides the first theoretical probe into the identifiability of hybrid-DGMs, and present meta-learning as a novel solution to construct identifiable hybrid-DGMs. On synthetic and real-data benchmarks, we provide strong empirical evidence for the un-identifiability of existing hybrid-DGMs using unconditional priors, and strong identifiability results of the presented meta-formulations of hybrid-DGMs.

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TMLR Journal 2024 Journal Article

On the Interdependence between Data Selection and Architecture Optimization in Deep Active Learning

Pradeep Bajracharya
Rui Li
Linwei Wang

Deep active learning (DAL) studies the optimal selection of labeled data for training deep neural networks (DNNs). While data selection in traditional active learning is mostly optimized for given features, in DNN these features are learned and change with the learning process as well as the choices of DNN architectures. How is the optimal selection of data affected by this change is not well understood in DAL. To shed light on this question, we present the first systematic investigation on: 1) the relative performance of representative modern DAL data selection strategies, as the architecture types and sizes change in the underlying DNN architecture (Focus 1), and 2) the effect of optimizing the DNN architecture of a DNN on DAL (Focus 2). The results suggest that the change in the DNN architecture significantly influences and outweighs the benefits of data selection in DAL. These results cautions the community in generalizing DAL findings obtained on specific architectures, while suggesting the importance to optimize the DNN architecture in order to maximize the effect of active data selection in DAL.

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EAAI Journal 2024 Journal Article

Thermal parameter inversion of low-heat cement concrete for Baihetan arch dam

Feng Wang
Rui Song
Hu Yu
Ao Zhang
Linwei Wang
Xia Chen

For the first time, low-heat cement was used in the entire dam section of Baihetan Dam, but the thermal properties of low-heat cement under construction conditions have yet to be fully studied. The thermal parameter values of low-heat cement may differ significantly from the indoor test values or specification values due to factors such as ambient temperature, cooling through water, and surface insulation under actual site conditions. Therefore, in order to obtain more accurate values of the thermal parameters, the hybrid swarm intelligence algorithm and field temperature monitoring data are used to identify the concrete thermal parameters of Baihetan arch dam. To overcome the shortcomings of Particle Swarm Optimization (PSO) that is easy to fall into local optimum and Artificial Bee Colony (ABC) that has insufficient development ability, an Integrated Algorithm Based on ABC and PSO (IABAP) is established. Through eight different test functions and comparing with other different algorithms, it is verified that the IABAP algorithm has certain advantages in terms of convergence speed and accuracy. Considering the influence of ambient air temperature and multi-shift water cooling during construction, IABAP is applied to the inversion of concrete thermal parameters with the same strength, different strength and different gradation of Baihetan arch dam. The computational results show the good performance of the IABAP algorithm in engineering applications on the one hand, and the applicability and reliability of the parameter inversion on the other hand, which can meet the accuracy requirements of practical engineering. At the same time, the conjecture that the thermal parameters are consistent in adjacent dam sections was verified by bringing the thermal parameters into the adjacent dam sections for simulation calculations. Finally, the experimental values of thermal parameters of low-heat cement concrete of Baihetan Dam were compared with the inverse values to analyze the change law of thermal parameters, and it was found that the final adiabatic temperature rise of low-heat concrete was smaller than the indoor experimental values during the actual construction.

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ICLR Conference 2023 Conference Paper

Continual Unsupervised Disentangling of Self-Organizing Representations

Zhiyuan Li 0007
Xiajun Jiang
Ryan Missel
Prashnna Kumar Gyawali
Nilesh Kumar
Linwei Wang

Limited progress has been made in continual unsupervised learning of representations, especially in reusing, expanding, and continually disentangling learned semantic factors across data environments. We argue that this is because existing approaches treat continually-arrived data independently, without considering how they are related based on the underlying semantic factors. We address this by a new generative model describing a topologically-connected mixture of spike-and-slab distributions in the latent space, learned end-to-end in a continual fashion via principled variational inference. The learned mixture is able to automatically discover the active semantic factors underlying each data environment and to accumulate their relational structure based on that. This distilled knowledge of different data environments can further be used for generative replay and guiding continual disentangling of new semantic factors. We tested the presented method on a split version of 3DShapes to provide the first quantitative disentanglement evaluation of continually learned representations, and further demonstrated its ability to continually disentangle new representations in benchmark datasets.

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ICLR Conference 2023 Conference Paper

Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting

Xiajun Jiang
Ryan Missel
Zhiyuan Li 0007
Linwei Wang

Modern applications increasingly require learning and forecasting latent dynamics from high-dimensional time-series. Compared to univariate time-series forecasting, this adds a new challenge of reasoning about the latent dynamics of an unobserved abstract state. Sequential latent variable models (LVMs) present an attractive solution, although existing works either struggle with long-term forecasting or have difficulty learning across diverse dynamics. In this paper, we first present a conceptual framework of sequential LVMs to unify existing works, contrast their fundamental limitations, and identify an intuitive solution to long-term forecasting for diverse dynamics via meta-learning. We then present the first framework of few-shot forecasting for high-dimensional time-series: instead of learning a single dynamic function, we leverage data of diverse dynamics and learn to adapt latent dynamic functions to few-shot support series. This is realized via Bayesian meta-learning underpinned by: 1) a latent dynamic function conditioned on knowledge derived from few-shot support series, and 2) a meta-model that learns to extract such dynamic-specific knowledge via feed-forward embedding of support set. We compared the presented framework with a comprehensive set of baseline models trained 1) globally on the large meta-training set with diverse dynamics, and 2) individually on single dynamics, both with and without fine-tuning to k-shot support series used by the meta-models. We demonstrated that the presented framework is agnostic to the latent dynamic function of choice and, at meta-test time, is able to forecast for new dynamics given variable-shot of support series.

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ICLR Conference 2020 Conference Paper

Progressive Learning and Disentanglement of Hierarchical Representations

Zhiyuan Li 0007
Jaideep Vitthal Murkute
Prashnna Kumar Gyawali
Linwei Wang

Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the concept of “starting small”, we present a strategy to progressively learn independent hierarchical representations from high- to low-levels of abstractions. The model starts with learning the most abstract representation, and then progressively grow the network architecture to introduce new representations at different levels of abstraction. We quantitatively demonstrate the ability of the presented model to improve disentanglement in comparison to existing works on two benchmark datasets using three disentanglement metrics, including a new metric we proposed to complement the previously-presented metric of mutual information gap. We further present both qualitative and quantitative evidence on how the progression of learning improves disentangling of hierarchical representations. By drawing on the respective advantage of hierarchical representation learning and progressive learning, this is to our knowledge the first attempt to improve disentanglement by progressively growing the capacity of VAE to learn hierarchical representations.

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