In the method section of our paper, we describe the general encoding-decoding paradigm. We provide a brief overview of our data preprocessing pipeline, which involves the following steps. We employ the method of Boussard et al. (2021) to estimate the location of Decentralized registration (Windolf et al., 2022) is applied to track and correct Figure 6: Motion drift in "good" and "bad" sorting recordings. "bad" sorting example, which is still affected by drift even after registration. To decode binary behaviors, such as the mouse's left or right choices, we utilize In this section, we provide visualizations to gain insights into the effectiveness of our proposed decoder.

Instead, we apply powerful machine learning models for one-class (Moya et al., 1993) or

Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of the bad images, and we call this task censoring. In this work, we present censored generation with a pre-trained diffusion model using a reward model trained on minimal human feedback. We show that censoring can be accomplished with extreme human feedback efficiency and that labels generated with a mere few minutes of human feedback are sufficient.