Search Results for author:
Found 14 papers, 6 papers with code
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
More importantly, we provide three extrapolation studies to demonstrate that PROSE-PDE can generalize physical features through the robust training of multiple operators and that the proposed model can extrapolate to predict PDE solutions whose models or data were unseen during the training.
MODNO: Multi Operator Learning With Distributed Neural Operators
Through a systematic study of five numerical examples, we compare the accuracy and cost of training a single neural operator for each operator independently versus training a MOL model using our proposed method.
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data.
Ranked #1 on
Binary Classification
on kickstarter
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems.
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers
Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling.
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
To enable DeepONets training with noisy data, we propose using the Bayesian framework of replica-exchange Langevin diffusion.
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
Consequently, it is more challenging to encode the key information sources--relevant contexts and entity types.
Ranked #1 on
Relation Extraction
on CDR
A deep neural network approach on solving the linear transport model under diffusive scaling
In our framework, the solution is approximated by a neural network that satisfies both the governing equation and other constraints.
Numerical Analysis Numerical Analysis
Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem
In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Computational multiscale methods for quasi-gas dynamic equations
This multiscale problem is interesting from a multiscale methodology point of view as the model problem has a hyperbolic multiscale term, and designing multiscale methods for hyperbolic equations is challenging.
Numerical Analysis Numerical Analysis 65M22, 65M60
paper2repo: GitHub Repository Recommendation for Academic Papers
Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.
Non-local Attention Learning on Large Heterogeneous Information Networks
In this way, it leverages both local and non-local information simultaneously.
Ranked #1 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
(Macro-F1 (60% training data) metric)
