We explore the intersection of LLMs and penetration testing to gain insight into their capabilities and challenges in the context of privilege escalation.
In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance.
We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature.
Additionally, the effect of sentiment on overtrading is observed to be more pronounced among individual investors in large-cap stocks compared to small- and mid-cap stocks.
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
To facilitate research in this new area, we build a richly annotated PSG-4D dataset consisting of 3K RGB-D videos with a total of 1M frames, each of which is labeled with 4D panoptic segmentation masks as well as fine-grained, dynamic scene graphs.
Our dataset includes 6, 742 drone images and 11 satellite maps, with metadata such as latitude, longitude, altitude, and capture date.
Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously.
We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources.
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on Recommendation Systems on Amazon-Book (HR@10 metric)