Reading lists for new LISA students(转)
Research in General
Basics of machine learning
Basics of deep learning
Practical recommendations for gradient-based training of deep architectures
Quick’n’dirty introduction to deep learning: Advances in Deep Learning
Contractive auto-encoders: Explicit invariance during feature extraction
An Analysis of Single Layer Networks in Unsupervised Feature Learning
The importance of Encoding Versus Training With Sparse Coding and Vector Quantization
Feedforward nets
“Improving Neural Nets with Dropout” by Nitish Srivastava
“What is the best multi-stage architecture for object recognition?”
MCMC
Radford Neal’s Review Paper (old but still very comprehensive)
Restricted Boltzmann Machines
Unsupervised learning of distributions of binary vectors using 2-layer networks
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machine
Enhanced Gradient for Training Restricted Boltzmann Machines
Using fast weights to improve persistent contrastive divergence
Training Products of Experts by Minimizing Contrastive Divergence
Boltzmann Machines
Deep Boltzmann Machines (Salakhutdinov & Hinton)
A Two-stage Pretraining Algorithm for Deep Boltzmann Machines
Regularized Auto-Encoders
Regularization
Stochastic Nets & GSNs
Others
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
What Regularized Auto-Encoders Learn from the Data Generating Distribution
Recurrent Nets
Learning long-term dependencies with gradient descent is difficult
Learning recurrent neural networks with Hessian-free optimization
On the importance of momentum and initialization in deep learning,
Long short-term memory (Hochreiter & Schmidhuber)
Long Short-Term Memory in Echo State Networks: Details of a Simulation Study
The "echo state" approach to analysing and training recurrent neural networks
Backpropagation-Decorrelation: online recurrent learning with O(N) complexity
New results on recurrent network training:Unifying the algorithms and accelerating convergence
Convolutional Nets
ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012.
Optimization issues with DL
Knowledge Matters: Importance of Prior Information for Optimization
Practical recommendations for gradient-based training of deep architectures
Hessian Free
Natural Gradient (TONGA)
NLP + DL
Distributed Representations of Words and Phrases and their Compositionality
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
CV+RBM
CV + DL
Scaling Up
DL + Reinforcement learning
Graphical Models Background
An Introduction to Graphical Models (Mike Jordan, brief course notes)
A View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants (Neal & Hinton, important paper to the modern understanding of Expectation-Maximization)
A Unifying Review of Linear Gaussian Models (Roweis & Ghahramani, ties together PCA, factor analysis, hidden Markov models, Gaussian mixtures, k-means, linear dynamical systems)
An Introduction to Variational Methods for Graphical Models (Jordan et al, mean-field, etc.)
Writing
Software documentation
Python, Theano, Pylearn2, Linux (bash) (at least the 5 first sections), git (5 first sections), github/contributing to it (Theano doc), vim tutorial or emacs tutorial
Software lists of built-in commands/functions
Other Software stuff to know about:
screen
ssh
ipython
matplotlib
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