A list of papers I used for my thesis about convolutional neural networks with a focus on batch normalization. The papers are mostly ordered chronologically in terms of their publication date. I divided the papers in sections Early work, Pre-AlexNet, Post-AlexNet, and Batch Normalization.
Learning Representations by Backpropagating Errors (Rumelhart et al 1986)
Invention of the backpropagation algorithm.
Handwritten Digit Recognition with a Backpropagation Network (LeCun et al 1990)
First paper on convolutional neural networks trained with backpropagation.
Gradient-based Learning Applied to Document Recogintion (LeCun et al 1998)
Overview of training end-to-end systems such as convolutional neural networks with gradient-based optimization.
Efficient Backprop (LeCun et al 1998)
Gives many practical recommendations for traning multi-layer (convolutional) neural networks.
- Motivates stochastic gradient descent with mini-batches
- Shows benefits of mean subtraction, normalization, and decorrelation
- Shows drawbacks of sigmoid activation function and motivates hyperbolic tangent (tanh)
- Proposes weight initialization scheme (LeCun initialization)
- Motivates use of adaptive optimization techniques and momentum
Greedy Layer-Wise Training of Deep Networks (Bengio et al 2006)
Introduces unsupervised pre-training and shows significant convergence improvements and generalization performance.
Understanding the Difficulty of Training Deep Feedforward Neural Networks (Glorot et al 2010)
Shows why training deep neural networks in deep networks is difficult and gives pointers for improvements.
- Gradient propagation study with sigmoid, tanh, and softsign
- New initialization scheme for these activations (Xavier initialization)
- Motivates the cross entropy loss function instead of mean squared error (MSE)
Deep Sparse Rectifier Neural Networks (Glorot et al 2011)
Shows the advantages of rectified activation functions (ReLU) for convergence speed.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (Duchi et al 2011)
Introduces adagrad, an adaptive optimization technique.
Practical Recommendations for Gradient-based Training of Deep Architectures (Bengio et al 2012)
Practical recommendations for setting hyperparameters such as the learning rate, learning rate decay, batch size, momentum, weight decay, and nonlinearity.
ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky et al 2012)
Breakthrough paper that popularized convolutional neural networks (namely AlexNet) and made the following contributions.
- The use of local response normalization
- Extensive use of regularizers such as data augmentation and dropout
Improving Neural Networks by Preventing Co-adaptation of Feature Detectors (Hinton et al 2012)
Describes dropout in detail.
Adadelta: An Adaptive Learning Rate Method (Zeiler et al 2012)
Introduces adadelta, an improved version of the adagrad adaptive optimization technique.
Maxout Networks (Goodfellow et al 2013)
Introduces the maxout neuron, a companion to dropout, that is able to approximate activation functions such as ReLU and the absolute value.
Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks (Saxe et al 2013)
Theoretical analysis of the dynamics in deep neural networks and proposal of the orthogonal initialization scheme.
On the Inportance of Initialization and Momentum in Deep Learning (Sutskever et al 2013)
Shows why careful weight initialization and (Nesterov) momentum accelerated SGD are cruciual for training deep neural networks.
Regularization of Neural Networks Using DropConnect (Wan et al 2013)
Introduces dropconnect, a generalization of dropout that drops random weights instead of entire neurons.
Visualizing and Understanding Convolution Networks (Zeiler et al 2013)
Introduces a novel visualization technique for convolutional filters using a method called deconvolution that maps layer activations back to the input pixel space.
Adam: A Method for Stochastic Optimization (Kingma et al 2014)
Introduces adam and adamax, improved versions of the adadelta adaptive optimization technique.
Going Deeper with Convolutions (Szegedy et al 2014)
Describes the inception architecture (GoogLeNet) that reduces the amount of learable parameters significantly while improving accuracy.
Very Deep Convolutional Networks for Large-scale Image Recognition (Simonyan et al 2014)
Motivates the use of architectures with smaller convolutional filters such as 1 x 1 and 3 x 3 (VGGNet).
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (He et al 2015)
Introduces a novel parametric rectifier (PReLU) and a weight initialization scheme tailored to rectified activations (Kaiming initialization).
Deep Residual Learning for Image Recognition (He et al 2015)
Describes a network architecture with residual connections (ResNet) that enable deeper architectures and are easier to optimize.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Ioffe et al 2015)
Introduces batch normalization, a method to accelerate deep network training by reducing the internal covariate shift. The authors claim batch normalization has the following properties.
- Enables higher learning rates and faster learning rate decay without the risk of divergence
- Regularizes the model by stabilizing the parameter growth
- Reduce the need for dropout, weight regularization, and local response normalization
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