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Found 4 papers, 2 papers with code
Fixed Point Diffusion Models
We introduce the Fixed Point Diffusion Model (FPDM), a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling.
Goodhart's Law in Reinforcement Learning
First, we propose a way to quantify the magnitude of this effect and show empirically that optimising an imperfect proxy reward often leads to the behaviour predicted by Goodhart's law for a wide range of environments and reward functions.
Wasserstein distributional robustness of neural networks
To evaluate the distributional robustness of neural networks, we propose a first-order AA algorithm and its multi-step version.
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset.
