The study focuses on the use of pre-trained language models for text-to-code generation. The researchers explore how sequence tokens can be adapted and represented differently depending on their modality. They…
Continue readingThe paper reviews the development and techniques of few-shot learning on graphs, a field that has seen significant advancements in recent years. The authors categorize existing studies into three major…
Continue readingThe study focuses on the challenge of structural divergence in pre-training and fine-tuning Graph Neural Networks (GNNs). It identifies the root cause as the discrepancy of generative patterns between pre-training…
Continue readingThis research investigates the ability of Large Language Models (LLMs) to reason with knowledge graphs they learned during pre-training. The study aims to understand the accuracy of LLMs in recalling…
Continue readingThis research paper presents a new method, named SAP, to improve Graph Neural Networks (GNNs). GNNs are effective at learning semantics of graph data. However, the structure information of a…
Continue readingThe paper introduces ROBOFUME, a system that allows robots to learn new tasks autonomously with minimal human intervention. The system pre-trains a multi-task manipulation policy using diverse datasets, and then…
Continue readingThe paper focuses on the Contrastive Language-Image Pre-training (CLIP) technique used in computer vision. The authors argue that the success of CLIP lies in its data, not its architecture or…
Continue readingEgoVLPv2 is a significant improvement over the previous generation of egocentric video-language pre-training (EgoVLP). It incorporates cross-modal fusion directly into the video and language backbones, learning strong video-text representation during…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
