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Found 5 papers, 1 papers with code
On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
Mitigating Biases with Diverse Ensembles and Diffusion Models
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut learning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones.
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones.
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
This phenomenon, also known as shortcut learning, is emerging as a key limitation of the current generation of machine learning models.
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes.
