This paper presents two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport problems, respectively.
This paper is available on arxiv under CC 4.0 license. Authors: Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.
edu; Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. This paper is available on arxiv under CC 4.0 license. Authors: Authors: Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.
Singapore Latest News, Singapore Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: ExperimentsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Hardware DetailsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Architecture OverviewThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Predictor AnalysisThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Conclusion & ReferencesThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Experimental ResultsThis paper investigates how the configuration of on-device hardware affects energy consumption for neural network inference with regular fine-tuning.
Read more »