179 research outputs found
Shared Risk Resource Group: Complexity and Approximability issues
International audienceThis article investigates complexity and approximability properties of combinatorial optimization problems yielded by the notion of Shared Risk Resource Group (SRRG). SRRG has been introduced in order to capture network survivability issues where a failure may break a whole set of resources, and has been formalized as colored graphs, where a set of resources is represented by a set of edges with same color. We consider here the analogous of classical problems such as determining paths or cuts with the minimum numbers of colors or color disjoint paths. These optimization problems are much more difficult than their counterparts in classical graph theory. In particular standard relationship such as the Max Flow - Min Cut equality do not hold any longer. In this article we identify cases where these problems are polynomial, for example when the edges of a given color form a connected subgraph, and otherwise give hardness and non approximability results for these problems
Do Generative Large Language Models need billions of parameters?
This paper presents novel systems and methodologies for the development of
efficient large language models (LLMs). It explores the trade-offs between
model size, performance, and computational resources, with the aim of
maximizing the efficiency of these AI systems. The research explores novel
methods that allow different parts of the model to share parameters, reducing
the total number of unique parameters required. This approach ensures that the
model remains compact without sacrificing its ability to learn and represent
complex language structures. This study provides valuable insights and tools
for creating more efficient and effective LLMs, contributing to a more
sustainable and accessible future for AI language modeling
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