CybAI news

arXiv:2407.04958v1 Announce Type: new Abstract: Normalizing Flows (NFs) have gained popularity among deep generative models due to their ability to provide exact likelihood estimation and efficient sampling. However, a crucial limitation of NFs is their substantial memory requirements, arising from maintaining the dimension of the latent space equal to that of the input space. Multi-scale architectures bypass this limitation by progressively reducing the dimension of latent variables while ensuring reversibility. Existing multi-scale architectures split the latent variables in a simple, static manner at the channel level, compromising NFs' expressive power. To address this issue, we propose a regularized and feature-dependent…

arXiv:2407.04920v1 Announce Type: new Abstract: In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments. Source link lol

arXiv:2407.05006v1 Announce Type: new Abstract: Research in the NLP sphere of the Turkic counterparts of Central Asian languages, namely Kazakh, Uzbek, Kyrgyz, and Turkmen, comes with the typical challenges of low-resource languages, like data scarcity and a general lack of linguistic resources. However, in the recent years research has greatly advanced via collection of language-specific datasets and development of downstream task technologies. Aiming to summarize this research up until May 2024, this paper also seeks to identify potential areas of future work. To achieve this, the paper gives a broad, high-level overview of the linguistic properties of the languages, the…

arXiv:2407.04949v1 Announce Type: new Abstract: Federated Learning is widely employed to tackle distributed sensitive data. Existing methods primarily focus on addressing in-federation data heterogeneity. However, we observed that they suffer from significant performance degradation when applied to unseen clients for out-of-federation (OOF) generalization. The recent attempts to address generalization to unseen clients generally struggle to scale up to large-scale distributed settings due to high communication or computation costs. Moreover, methods that scale well often demonstrate poor generalization capability. To achieve OOF-resiliency in a scalable manner, we propose Topology-aware Federated Learning (TFL) that leverages client topology - a graph representing client…