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Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
Recent advances in the field of artificial intelligence have been made p...
02/12/2020 ∙ by Mohammad Saeed Abrishami, et al. ∙
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Run-time Deep Model Multiplexing
We propose a framework to design a light-weight neural multiplexer that ...
01/14/2020 ∙ by Amir Erfan Eshratifar, et al. ∙
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Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
Small inter-class and large intra-class variations are the main challeng...
09/06/2019 ∙ by Amir Erfan Eshratifar, et al. ∙
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BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services
Recent studies have shown the latency and energy consumption of deep neu...
02/04/2019 ∙ by Amir Erfan Eshratifar, et al. ∙
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Towards Collaborative Intelligence Friendly Architectures for Deep Learning
Modern mobile devices are equipped with high-performance hardware resour...
02/01/2019 ∙ by Amir Erfan Eshratifar, et al. ∙
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Gradient Agreement as an Optimization Objective for Meta-Learning
This paper presents a novel optimization method for maximizing generaliz...
10/18/2018 ∙ by Amir Erfan Eshratifar, et al. ∙
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A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply k...
09/21/2018 ∙ by Amir Erfan Eshratifar, et al. ∙
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JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Deep neural networks are among the most influential architectures of dee...
01/25/2018 ∙ by Amir Erfan Eshratifar, et al. ∙
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A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
With ever-increasing application of machine learning models in various d...
01/11/2018 ∙ by Mahdi Nazemi, et al. ∙
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