For heterophilic graphs, achieving balance based on relative gradients leads to faster training and better generalization. In contrast, homophilic graphs benefit from delaying the learning of later layers.

Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost.

Table 7: Examples of Generated Cartoon Descriptions Type of descriptions GPT -4o Human Written [20] Canny description A knight in armor is riding a horse, holding a lance with a traffic light on top. A line of businessmen in suits follows behind him. There are two men on a horse. They are wearing soldier outfits. Uncanny Description It's unusual to see a medieval knight leading modern businessmen as if going into battle.

As we conclude this extensive data collection effort, we release the entire preference dataset to the research community, fostering further advancements in AI humor generation and evaluation.

Overall, our method can generate high-fidelity, diverse, and multi-view consistent meshes from single-view wild images within 30 seconds, as shown in Figure 1. We conduct extensive experiments on various wild 2D images with different styles.

Entropy neurons are characterized by an unusually high weight norm and influence the final layer normalization (LayerNorm) scale to effectively scale down the logits.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

EHRNoteQA in two formats: open-ended and multi-choice question answering, and propose a reliable evaluation method for each. We evaluate 27 LLMs using EHRNoteQA and examine various factors affecting the model performance ( e.g., the length and number of discharge summaries).