info coming from bengio
NLP + DL
[1] Natural Language Processing (Almost) from Scratch
[2] DeViSE: A Deep Visual-Semantic Embedding Model
[3] Distributed Representations of Words and Phrases and their Compositionality
[4] Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
CV+RBM
[2] What makes a good model of natural images?
[3] Phone Recognition with the mean-covariance restricted Boltzmann machine
[4] Unsupervised Models of Images by Spike-and-Slab RBMs
CV + DL
[1] Imagenet classification with deep convolutional neural networks
Scaling Up
[1] Large Scale Distributed Deep Networks
[2] Random search for hyper-parameter optimization
[3] Practical Bayesian Optimization of Machine Learning Algorithms
DL + Reinforcement learning
[1] Playing Atari with Deep Reinforcement Learning (paper not officially released yet!)
Graphical Models Background
[1] An Introduction to Graphical Models (Mike Jordan, brief course notes)
[2] A View of the EM Algorithm that Justifies Incremental, Sparse and Other Variants (Neal & Hinton, important paper to the modern understanding of Expectation-Maximization)
[3] A Unifying Review of Linear Gaussian Models (Roweis & Ghahramani, ties together PCA, factor analysis, hidden Markov models, Gaussian mixtures, k-means, linear dynamical systems)
[4] An Introduction to Variational Methods for Graphical Models (Jordan et al, mean-field, etc.)
Writing
[1] Writing a great research paper (video of the presentation)
Software documentation
[2] Linux(bash) (at least the 5 first sections), git (5 first sections), github/contributing to it (Theano doc).
[3] vim tutorial or emacs tutorial
Software lists of built-in commands/functions
[1] Bash commands
[2] List of Built-in Python Functions
[3] vim commands
Other Software stuff to know about:
[1] screen
[2] ssh
[3] ipython
[4] matplotlib
[...]