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11/21/2022
Learning Low-Rank Representations for Model Compression
Vector Quantization (VQ) is an appealing model compression method to obt...
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11/17/2022
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
The superior performance of modern deep networks usually comes at the pr...
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07/08/2021
Collaboration of Experts: Achieving 80 100M FLOPs
In this paper, we propose a Collaboration of Experts (CoE) framework to ...
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04/08/2021
Learning specialized activation functions with the Piecewise Linear Unit
The choice of activation functions is crucial for modern deep neural net...
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03/29/2021
FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
Weight decay is a widely used technique for training Deep Neural Network...
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10/20/2020
AutoBSS: An Efficient Algorithm for Block Stacking Style Search
Neural network architecture design mostly focuses on the new convolution...
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04/22/2020
DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks
Convolution operator is the core of convolutional neural networks (CNNs)...
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12/24/2019
BETANAS: BalancEd TrAining and selective drop for Neural Architecture Search
Automatic neural architecture search techniques are becoming increasingl...
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12/24/2019
Adversarial AutoAugment
Data augmentation (DA) has been widely utilized to improve generalizatio...
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12/13/2018
IRLAS: Inverse Reinforcement Learning for Architecture Search
In this paper, we propose an inverse reinforcement learning method for a...
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11/06/2018
Synaptic Strength For Convolutional Neural Network
Convolutional Neural Networks(CNNs) are both computation and memory inte...
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08/16/2018
