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Found 10 papers, 3 papers with code
Governing equation discovery of a complex system from snapshots
Complex systems in physics, chemistry, and biology that evolve over time with inherent randomness are typically described by stochastic differential equations (SDEs).
Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
Our results suggest that feeding the prior knowledge of the underlying system and the mathematical theory appropriately to the learning framework can reinforce the capability of machine learning in solving physical problems.
Switched Flow Matching: Eliminating Singularities via Switching ODEs
Continuous-time generative models, such as Flow Matching (FM), construct probability paths to transport between one distribution and another through the simulation-free learning of the neural ordinary differential equations (ODEs).
From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems
We then incorporate the estimated spatial gradients as additional inputs to a neural network.
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Particularly, we propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
Neural Delay Differential Equations: System Reconstruction and Image Classification
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with representative datasets.
Neural Piecewise-Constant Delay Differential Equations
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems.
Neural Delay Differential Equations
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets.
