81 citations · 181 across the 14 of their papers we have counts for
16 papers
Phase Matching for Out-of-Distribution Generalization
Chengming Hu, Yeqian Du, Rui Wang +2
The Fourier transform, an explicit decomposition method for visual signals, has been employed to explain the out-of-distribution generalization behaviors of Deep Neural Networks (D…
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
Yuexiao Ma, Huixia Li, Xiawu Zheng +6
Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We ar…
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Rui Wang, Yihe Dong, Sercan Ö. Arik +1
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (D…
Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting
Rui Wang, Robin Walters, Rose Yu
Exploiting symmetry in dynamical systems is a powerful way to improve the generalization of deep learning. The model learns to be invariant to transformation and hence is more robu…
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Rui Wang, Robin Walters, Rose Yu
Incorporating symmetry as an inductive bias into neural network architecture has led to improvements in generalization, data efficiency, and physical consistency in dynamics modeli…
Latent Space Simulation for Carbon Capture Design Optimization
Brian Bartoldson, Rui Wang, Yucheng Fu +5
The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational chal…
Discriminating modelling approaches for Point in Time Economic Scenario Generation
Rui Wang
We introduce the notion of Point in Time Economic Scenario Generation (PiT ESG) with a clear mathematical problem formulation to unify and compare economic scenario generation appr…
BAGUA: Scaling up Distributed Learning with System Relaxations
Shaoduo Gan, Xiangru Lian, Rui Wang +11
Recent years have witnessed a growing list of systems for distributed data-parallel training. Existing systems largely fit into two paradigms, i.e., parameter server and MPI-style…
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Rui Wang, Rose Yu
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigi…
Meta-Learning Dynamics Forecasting Using Task Inference
Rui Wang, Robin Walters, Rose Yu
Current deep learning models for dynamics forecasting struggle with generalization. They can only forecast in a specific domain and fail when applied to systems with different para…
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Rui Wang, Danielle Maddix, Christos Faloutsos +2
How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but…
Aortic Pressure Forecasting with Deep Sequence Learning
Eliza Huang, Rui Wang, Uma Chandrasekaran +1
Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of t…
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley D. Edwards, Himanshu Sahni, Rosanne Liu +7
In this paper, we introduce a novel form of value function, , that expresses the utility of transitioning from a state to a neighboring state and then acting opt…
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Rui Wang, Robin Walters, Rose Yu
Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to general…
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Rui Wang, Karthik Kashinath, Mustafa Mustafa +2
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, t…
First-Order Preconditioning via Hypergradient Descent
Ted Moskovitz, Rui Wang, Janice Lan +4
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space.These difficulti…