Abstract: We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational ...

With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as ...

ABSTRACT: Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

ABSTRACT: Graph burning is a model for describing the spread of influence in social networks and the generalized burning number b r ( G ) of graph G is a parameter to measure the speed of information ...

In the rapidly evolving landscape of machine learning and artificial intelligence, understanding the fundamental representations within transformer models has emerged as a critical research challenge.

Manipulation of an AI model’s graph can be used to implant codeless, persistent backdoors in ML models, AI security firm HiddenLayer reports. Dubbed ShadowLogic, the technique relies on manipulating a ...

Traffic forecasting is a fundamental aspect of smart city management, essential for improving transportation planning and resource allocation. With the rapid advancement of deep learning, complex ...

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