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Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training ...
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DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Learning deep representations to solve complex machine learning tasks ha...
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ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
Typical deep convolutional architectures present an increasing number of...
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BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Neural networks have demonstrably achieved state-of-the art accuracy usi...
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Training Modern Deep Neural Networks for Memory-Fault Robustness
Because deep neural networks (DNNs) rely on a large number of parameters...
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Efficient Hardware Implementation of Incremental Learning and Inference on Chip
In this paper, we tackle the problem of incrementally learning a classif...
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Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
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Attention Based Pruning for Shift Networks
In many application domains such as computer vision, Convolutional Layer...
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Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectu...
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Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous co...
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Transfer Incremental Learning using Data Augmentation
Deep learning-based methods have reached state of the art performances, ...
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