Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness.

Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the proliferation of methods designed for this emerging task, the lack of standard experimental settings and fair performance comparisons makes it challenging to understand which and when models perform well across different scenarios.

In addition to sequence-based approaches, structure-based encoders have emerged to leverage the 3D structural information of proteins.

In this paper, we introduce VidProM, the first large-scale dataset comprising 1.67 M illion unique text-to-Vid eo Pro mpts from real users.

Our method comes close to the original models' performance in cross-lingual and

Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.

Positional encoding plays a crucial role in transformers, significantly impacting model performance and length generalization. Prior research has introduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences.

In this work, we introduce ChatQA, a suite of models that outperform GPT -4 on retrieval-augmented generation (RAG) and conversational question answering (QA).