Abstract: Real-world software–hardware co-design for AI accelerators must meet strict constraints on accuracy and PPA, making design space exploration both costly and inefficient. In this work, we ...
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
The novel method leverages canonical correlation guided deep neural network-based technology for effective data fusion. Experimental results demonstrate superior performance across benchmark tasks, ...
Develop optimal solutions to a scheduling problem by modelling it as a Constraint Satisfaction Problem (CSP), a method used widely in the field of Artificial Intelligence. I've open-sourced Delegator ...
Bayesian optimization is an effective framework for identifying optimal control parameters of excavation machinery from a limited number of costly trials. While it can improve excavation performance ...
Many Bayesian Optimization users report that they don’t like that it can’t deal with uncertain factor ranges, lack of availability of rare materials at some stages of experimentation, constrained ...
ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
At-a-Glance: Refinery automation integrates DCS/PLC/SIS, analyzers, APC/MPC, and real-time optimization to maximize throughput, cut energy and giveaway, and protect assets. The core is ...
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