This are an assortment of links that I have come across while doing research. They are all in one way or another related to my research. I hope they are useful to other people as they have been useful to me.

Machine Learning

• dSP: Distributed structured prediction.
• LIBLINEAR: Linear SVM.
• LIBSVM: Non-linear SVM.
• Kaggle: Machine learning competitions.
• CMA-ES: Global black box function optimizer.

Deep Learning

Frameworks

• Torch7 (github, cheatsheet): Flexible deep learning framework in Lua.
• Caffe (github, model zoo): Deep learning framework in C++ using model files.
• Theano (github): Deep learning framework in python that does symbolic compilation.
• TensorFlow (github): Google’s distributed deep learning framework.

Implementations

• Reinforcement Learning for Atari Games: Implementation of various approaches for reinforcement learning of atari games.
• Deep Residual Networks (torch: ILSVRC2015 winning network. Learns residual functions.
• Generative Adversial Networks (GAN) (torch): Image generation with deep networks.
• DecoupledNet: Semantic segmentation system.
• Neuraltalk (github): Image to text (Stanford).
• Show, Attend and Tell: Image to text (Toronto).
• Draw: Teaching network to draw.
• torch lstm (tree lstm: Long Short Term Memory unit implementation.
• R-CNN (Fast R-CNN, Faster R-CNN): Detection with convolutional networks.
• char-RNN (github): Character prediction with recursive neural networks.
• Learning to execute: Recursive neural network learns to generate python code.
• Deep Q-Network (Caffe, torch): Deep reinforcement learning for atari games.
• eyescream: Generating natural images with CNN.
• Deep Dreaming (github, torch, Caffe): Generating Neural Network art.
• DeepWalk (github): Deep learning for graphs.
• DeepPose: 2D pose estimation with CNN.
• Deep Filter Banks: Filter banks for texture recognition.
• Deep Convolutional Inverse Graphics Network (github): Encoder-decoder architecture that learns a graphics engine.
• Triplet Network: Learns from three examples simultaneously.
• Deconvolution Network for Segmentation (github): Encoder-decoder architectures for semantic segmentation.
• Deep Pink (github): Deep chess AI.
• Uncertainty in Deep Networks (github caffe, demos): Visualizing network uncertainty.
• Neural Artwork (github 1, github 2): Generating artwork with neural networks.

Computer Vision

Frameworks

• VLfeat (github): SIFT descriptors and other tools. Matlab/C interface.
• OpenCV: Large general usage computer vision library in C++.

Face Recognition

• OpenFace: Full pipeline for face recognition using deep networks.
• IntraFace: Face attributes, feature detection, expression and expression transfer.
• VGG Face Descriptor: Deep network-based face descriptor.

Tools

• gPb superpixels: Large good quality superpixels.
• SLIC superpixels (opencv implementation): Fast regular superpixels.
• 9 superpixel algorithms (github): Benchmark of various superpixel algorithms.
• DAISY descriptor: Dense fast descriptor.
• Geodesic Object Proposals (3rd party github): Good object proposal approach.
• Image Specificity (github): Determine if an image is specific or not.
• SRCNN (github, waifu2x torch implementation): Super Resolution Convolutional Neural Network.
• Visual SfM: Frontend to some well known Structure from Motion (SfM) algorithms.

Datasets

• Places (Places2): Scene classification dataset.
• Cross-Age Celebrity Dataset: Celebrity faces at different ages.
• CelebA dataset: Large amount of celebrity face images with landmark and attribute annotations.
• MovieBook and BookCorpus: Large book corpus and books aligned with movies.
• MPII Movie Description: Movies with caption dataset.
• Microsoft COCO: Segmentation, detection and text generation dataset.
• MPII Human Pose: 2D human pose estimation and activity dataset.
• KITTI: Autonomous driving related dataset (stereo, flow, odometry, detection, tracking, etc…).
• Fashionista: Clothes parsing dataset.
• Human3.6M: 3D human pose estimation dataset.
• Imagenet: Image classification dataset.
• CIFAR: 32x32 pixel image classification dataset.
• TinyImages: Really big 32x32 pixel image dataset.

Interesting

• drawNet: See what’s going on in a network.

Natural Language Processing

• lda2vec: Modelling both word-to-word and document-to-word relationships jointly.
• Skip-thought vectors: Sentence encoding trained on books.
• sentence2vew (word2vec): Generate feature vectors from sentences or words.
• Stanford Sentiment Analysis: See how positive reviews are.

Manifolds

• manopt: General optimization on manifolds.
• smanifold: Exponential and logarithmic map for implicitly defined manifolds for principal geodesic analysis.

Miscellaneous

• LaTeX beamer mtheme: Good theme for presentations.
• The Elements of Style: Must read for good writing.

Other Lists

• Computer Vision.
• Machine Learning.
• Deep Learning.
• Torch7.