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Explicitly Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (DARTS) has gained much succes...
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Data Agnostic Filter Gating for Efficient Deep Networks
To deploy a well-trained CNN model on low-end computation edge devices, ...
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Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers
Quantum error mitigation techniques are at the heart of quantum computat...
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ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Neural architecture search (NAS) aims to produce the optimal sparse solu...
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Quantum differentially private sparse regression learning
Differentially private (DP) learning, which aims to accurately extract p...
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GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet
Training a supernet matters for one-shot neural architecture search (NAS...
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Bringing Giant Neural Networks Down to Earth with Unlabeled Data
Compressing giant neural networks has gained much attention for their ex...
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Privileged Multi-label Learning
This paper presents privileged multi-label learning (PrML) to explore an...
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Parts for the Whole: The DCT Norm for Extreme Visual Recovery
Here we study the extreme visual recovery problem, in which over 90% of ...
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Streaming Label Learning for Modeling Labels on the Fly
It is challenging to handle a large volume of labels in multi-label lear...
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