CybAI news

Data catalogs and metadata catalogs share some similarities, particularly in their nearly identical names. And while they have some common functions, there are also important differences between the two entities that big data practitioners should know about. Metadata catalogs, which are sometimes called metastores or technical data catalogs, have been in the news lately. If you’re a regular Datanami reader (and we certainly hope you are!), you would have read a lot metadata catalogs at the Snowflake and Databricks conferences last month, when the two competitors committed to open sourcing their respective metadata catalogs, Polaris and Unity Catalog. So what…

arXiv:2407.01596v1 Announce Type: new Abstract: This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes made up with irregular shaped walls. Due to the use of different shapes for the walls, a classification model trained in one maze that captures its structure does not generalize for the other. This issue is resolved by adopting FL framework between the robots that explore only one maze so that the collective knowledge allows them…

arXiv:2407.01636v1 Announce Type: new Abstract: This work aims to tackle the all-in-one image restoration task, which seeks to handle multiple types of degradation with a single model. The primary challenge is to extract degradation representations from the input degraded images and use them to guide the model's adaptation to specific degradation types. Recognizing that various degradations affect image content differently across frequency bands, we propose a new all-in-one image restoration approach from a frequency perspective, leveraging advanced vision transformers. Our method consists of two main components: a frequency-aware Degradation prior learning transformer (Dformer) and a degradation-adaptive Restoration transformer (Rformer). The…

arXiv:2407.01602v1 Announce Type: new Abstract: Transformers are extremely successful machine learning models whose mathematical properties remain poorly understood. Here, we rigorously characterize the behavior of transformers with hardmax self-attention and normalization sublayers as the number of layers tends to infinity. By viewing such transformers as discrete-time dynamical systems describing the evolution of points in a Euclidean space, and thanks to a geometric interpretation of the self-attention mechanism based on hyperplane separation, we show that the transformer inputs asymptotically converge to a clustered equilibrium determined by special points called leaders. We then leverage this theoretical understanding to solve sentiment analysis problems…