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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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publications
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Phillip Si, Allan Bishop, Volodymyr Kuleshov
Spotlight, Published in International Conference on Learning Presentations, 2022
Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of normalizing flow models trained using a novel objective based on proper scoring rules. Our objective does not require calculating computationally expensive determinants of Jacobians during training and supports new types of neural architectures, such as neural autoregressive flows from which it is easy to sample. We leverage these models in quantile flow regression, an approach that parameterizes predictive conditional distributions with flows, resulting in improved probabilistic predictions on tasks such as time series forecasting and object detection. Our novel objective functions and neural flow parameterizations also yield improvements on popular generation and density estimation tasks, and represent a step beyond maximum likelihood learning of flows.
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
Seokmin Choi, Sajad Mousavi, Phillip Si, Haben G. Yhdego, Fatemeh Khadem, Fatemeh Afghah
Published in Arxiv, 2023
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a self-supervised representation learning approach that unlocks the underlying language of ECGs. By unsupervised pre-training of the model, we mitigate challenges posed by the lack of well-labeled and curated medical data. ECGBERT, inspired by advances in the area of natural language processing and large language models, can be fine-tuned with minimal additional layers for various ECG-based problems. Through four tasks, including Atrial Fibrillation arrhythmia detection, heartbeat classification, sleep apnea detection, and user authentication, we demonstrate ECGBERT’s potential to achieve state-of-the-art results on a wide variety of tasks.
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
Published in International Conference on Machine Learning, 2023
Training normalizing flow generative models can be challenging due to the need to calculate computationally expensive determinants of Jacobians. This paper studies the likelihood-free training of flows and proposes the energy objective, an alternative sample-based loss based on proper scoring rules. The energy objective is determinant-free and supports flexible model architectures that are not easily compatible with maximum likelihood training, including semi-autoregressive energy flows, a novel model family that interpolates between fully autoregressive and non-autoregressive models. Energy flows feature competitive sample quality, posterior inference, and generation speed relative to likelihood-based flows; this performance is decorrelated from the quality of log-likelihood estimates, which are generally very poor. Our findings question the use of maximum likelihood as an objective or a metric, and contribute to a scientific study of its role in generative modeling.
LD-EnSF: Synergizing Latent Dynamics with Ensemble Score Filters for Fast Data Assimilation with Sparse Observations
Pengpeng Xiao, Phillip Si, Peng Chen
Published in Arxiv, 2024
Data assimilation techniques are crucial for correcting the trajectory when modeling complex physical systems. A recently developed data assimilation method, Latent Ensemble Score Filter (Latent-EnSF), has shown great promise in addressing the key limitation of EnSF for highly sparse observations in high-dimensional and nonlinear data assimilation problems. It performs data assimilation in a latent space for encoded states and observations in every assimilation step, and requires costly full dynamics to be evolved in the original space. In this paper, we introduce Latent Dynamics EnSF (LD-EnSF), a novel methodology that completely avoids the full dynamics evolution and significantly accelerates the data assimilation process, which is especially valuable for complex dynamical problems that require fast data assimilation in real time. To accomplish this, we introduce a novel variant of Latent Dynamics Networks (LDNets) to effectively capture and preserve the system’s dynamics within a very low-dimensional latent space. Additionally, we propose a new method for encoding sparse observations into the latent space using Long Short-Term Memory (LSTM) networks, which leverage not only the current step’s observations, as in Latent-EnSF, but also all previous steps, thereby improving the accuracy and robustness of the observation encoding. We demonstrate the robustness, accuracy, and efficiency of the proposed method for two challenging dynamical systems with highly sparse (in both space and time) and noisy observations.
Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data
Phillip Si, Peng Chen
Published in International Conference on Learning Presentations, 2025
Accurate modeling and prediction of complex physical systems often rely on data assimilation techniques to correct errors inherent in model simulations. Traditional methods like the Ensemble Kalman Filter (EnKF) and its variants as well as the recently developed Ensemble Score Filters (EnSF) face significant challenges when dealing with high-dimensional and nonlinear Bayesian filtering problems with sparse observations, which are ubiquitous in real-world applications. In this paper, we propose a novel data assimilation method, Latent-EnSF, which leverages EnSF with efficient and consistent latent representations of the full states and sparse observations to address the joint challenges of high dimensionlity in states and high sparsity in observations for nonlinear Bayesian filtering. We introduce a coupled Variational Autoencoder (VAE) with two encoders to encode the full states and sparse observations in a consistent way guaranteed by a latent distribution matching and regularization as well as a consistent state reconstruction. With comparison to several methods, we demonstrate the higher accuracy, faster convergence, and higher efficiency of Latent-EnSF for two challenging applications with complex models in shallow water wave propagation and medium-range weather forecasting, for highly sparse observations in both space and time.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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