The first dimension is the most fundamental: statistical fidelity. It is not enough for synthetic data to look random. It must behave like real data. This means your distributions, cardinalities, and ...
Google’s ATLAS study reveals how languages help each other in AI training, offering scaling laws and pairing insights for better multilingual models.
Abstract: Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned ...
Background: Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseases, proteins), providing valuable ...
Abstract: Graph topology inference is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneous.
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