Based on https://github.com/robertsdionne/neural-network-papers

## Table of Contents

- Other Lists
- Surveys
- Books
- Datasets
- Pretrained Models
- Programming Frameworks
- Learning to Compute
- Natural Language Processing
- Convolutional Neural Networks
- Recurrent Neural Networks
- Convolutional Recurrent Neural Networks
- Adversarial Neural Networks
- Autoencoders
- Restricted Boltzmann Machines
- Biologically Plausible Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Theory
- Quantum Computing
- Training Innovations
- Parallel Training
- Weight Compression
- Numerical Precision
- Numerical Optimization
- Motion Planning
- Simulation
- Hardware
- Cognitive Architectures
- Computational Creativity
- Cryptography
- Distributed Computing
- Clustering
- Other

Other Lists

- DeepLearning.University – An Annotated Deep Learning Bibliography | Memkite(github.com/memkite/DeepLearningBibliography)
- Deep Learning for NLP resources
- Reading List « Deep Learning
- Reading lists for new MILA students
- Awesome Recurrent Neural Networks
- Awesome Deep Learning
- Deep learning Reading List
- A curated list of speech and natural language processing resources (github.com/edobashira/speech-language-processing)
- CS089/CS189 | Deep Learning | Spring 2015

## Surveys

- Deep Learning
- Deep Learning in Neural Networks: An Overview
- Deep neural networks: a new framework for modelling biological vision and brain information processing
- A Primer on Neural Network Models for Natural Language Processing
- Natural Language Understanding with Distributed Representation

## Books

## Datasets

- Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (fb.ai/babi)
- Teaching Machines to Read and Comprehend (github.com/deepmind/rc-data)
- One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling (github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark)
- The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems(cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0)
- Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books(BookCorpus)
- Every publicly available Reddit comment, for research.
- Stack Exchange Data Dump
- Europarl: A Parallel Corpus for Statistical Machine Translation (www.statmt.org/europarl/)
- RTE Knowledge Resources

## Pretrained Models

## Programming Frameworks

- TensorFlow (tensorflow.org) (github.com/tensorflow/tensorflow)
- Caffe: Convolutional Architecture for Fast Feature Embedding (github.com/BVLC/caffe) (github.com/amd/OpenCL-caffe)
- Theano: A CPU and GPU Math Compiler in Python (github.com/Theano/Theano)
- Torch7: A Matlab-like Environment for Machine Learning (github.com/torch/distro)
- Brainstorm
- Deeplearning4j – Open-source, distributed deep learning for the JVM (github.com/deeplearning4j/deeplearning4j)
- linalg: Matrix Computations in Apache Spark
- cuDNN: Efficient Primitives for Deep Learning
- Fast Convolutional Nets With fbfft: A GPU Performance Evaluation (github.com/facebook/fbcuda)
- Guide to NumPy
- Probabilistic Programming in Python using PyMC

## Learning to Compute

- Neural GPUs Learn Algorithms
- A Roadmap towards Machine Intelligence
- On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
- Binding via Reconstruction Clustering
- Neural Random-Access Machines
- Learning Simple Algorithms from Examples
- Neural Programmer: Inducing Latent Programs with Gradient Descent
- Neural Programmer-Interpreters
- Neural Turing Machines
- Memory Networks (github.com/facebook/MemNN)
- Learning to Transduce with Unbounded Memory
- Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets (github.com/facebook/Stack-RNN)
- Feedforward Sequential Memory Neural Networks without Recurrent Feedback
- Pointer Networks
- On End-to-End Program Generation from User Intention by Deep Neural Networks
- Deep Knowledge Tracing (github.com/chrispiech/DeepKnowledgeTracing)
- Learning to Execute
- Tree-structured composition in neural networks without tree-structured architectures
- Grammar as a Foreign Language
- Learning To Learn Using Gradient Descent
- Learning to control fast-weight memories: An alternative to recurrent nets(ftp://ftp.idsia.ch/pub/juergen/fastweights.ps.gz)
- An introspective network that can learn to run its own weight change algorithm(ftp://ftp.idsia.ch/pub/juergen/iee93self.ps.gz)
- Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements
- Optimal Ordered Problem Solver (ftp://ftp.idsia.ch/pub/juergen/oopsmlj.pdf)
- The Fastest and Shortest Algorithm for All Well-Defined Problems (ftp://ftp.idsia.ch/pub/techrep/IDSIA-16-00.ps.gz)
- The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions(ftp://ftp.idsia.ch/pub/juergen/coltspeed.pdf)
- Learning Game of Life with a Convolutional Neural Network (github.com/DanielRapp/cnn-gol)

## Natural Language Processing

- Text Understanding from Scratch
- Deep Learning, NLP, and Representations
- Language Models for Image Captioning: The Quirks and What Works
- Zero-Shot Learning Through Cross-Modal Transfer
- On Using Very Large Target Vocabulary for Neural Machine Translation
- BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
- Deep Unordered Composition Rivals Syntactic Methods for Text Classification

### Word Vectors

- So similar and yet incompatible: Toward automated identification of semantically compatible words(github.com/germank/compatibility-naacl2015)
- Controlled Experiments for Word Embeddings (github.com/benjaminwilson/word2vec-norm-experiments)
- Natural Language Processing (almost) from Scratch
- Efficient Estimation of Word Representations in Vector Space
- GloVe: Global Vectors for Word Representation
- Learning to Understand Phrases by Embedding the Dictionary
- Inverted indexing for cross-lingual NLP
- Random walks on discourse spaces: a new generative language model with applications to semantic word embeddings
- Breaking Sticks and Ambiguities with Adaptive Skip-gram
- Language Recognition using Random Indexing

### Sentence and Paragraph Vectors

- Generating Sentences from a Continuous Space
- Distributed Representations of Sentences and Documents
- Document Embedding with Paragraph Vectors
- A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
- Skip-Thought Vectors (github.com/ryankiros/skip-thoughts)
- From Word Embeddings To Document Distances

### Character Vectors

- Alternative structures for character-level RNNs
- Character-based Neural Machine Translation
- Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation(github.com/wlin12/JNN)
- Character-Aware Neural Language Models (github.com/yoonkim/lstm-char-cnn)
- Modeling Order in Neural Word Embeddings at Scale
- Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs

### Attention Mechanisms

- Neural Machine Translation by Jointly Learning to Align and Translate
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
- Attention with Intention for a Neural Network Conversation Model

### Sequence-to-Sequence Learning

- Multi-task Sequence to Sequence Learning
- Order Matters: Sequence to sequence for sets
- Task Loss Estimation for Sequence Prediction
- Semi-supervised Sequence Learning
- A Hierarchical Neural Autoencoder for Paragraphs and Documents (github.com/jiweil/Hierarchical-Neural-Autoencoder)
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Neural Transformation Machine: A New Architecture for Sequence-to-Sequence Learning
- On Using Monolingual Corpora in Neural Machine Translation

### Language Understanding

- Reasoning about Entailment with Neural Attention
- The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations
- Investigation of Recurrent-Neural-Network Architectures and Learning Methods for Spoken Language Understanding
- Language Understanding for Text-based Games Using Deep Reinforcement Learning (github.com/karthikncode/text-world-player)

### Question Answering, and Conversing

- A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language(github.com/golosio/annabell)
- Large-scale Simple Question Answering with Memory Networks
- Reasoning in Vector Space: An Exploratory Study of Question Answering
- Deep Learning for Answer Sentence Selection
- Neural Responding Machine for Short-Text Conversation
- A Neural Conversational Model
- VQA: Visual Question Answering
- Question Answering with Subgraph Embeddings
- Hierarchical Neural Network Generative Models for Movie Dialogues
- Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
- Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

### Convolutional

- Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks.(github.com/davek44/Basset)
- A Convolutional Neural Network for Modelling Sentences
- Convolutional Neural Networks for Sentence Classification (github.com/yoonkim/CNN_sentence)
- Text Understanding from Scratch
- DeepWriterID: An End-to-end Online Text-independent Writer Identification System
- Encoding Source Language with Convolutional Neural Network for Machine Translation
- Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
- Convolutional Neural Network Architectures for Matching Natural Language Sentences

### Recurrent

- Long Short-Term Memory Over Tree Structures
- Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
- CCG Supertagging with a Recurrent Neural Network

## Convolutional Neural Networks

- Spatial Transformer Networks
- SimNets: A Generalization of Convolutional Networks
- Fast Algorithms for Convolutional Neural Networks
- Striving for Simplicity: The All Convolutional Net
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Very Deep Multilingual Convolutional Neural Networks for LVCSR
- Network In Network
- Going Deeper with Convolutions (github.com/google/inception)
- Convolutional Networks on Graphs for Learning Molecular Fingerprints (github.com/HIPS/neural-fingerprint)
- Deep Learning for Single-View Instance Recognition
- Learning to Generate Chairs with Convolutional Neural Networks (github.com/stokasto/caffe/tree/chairs_deconv)
- Deep Convolutional Inverse Graphics Network
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- Long-term Recurrent Convolutional Networks for Visual Recognition and Description
- A Machine Learning Approach for Filtering Monte Carlo Noise
- Image Super-Resolution Using Deep Convolutional Networks
- Learning to Deblur
- Monocular Object Instance Segmentation and Depth Ordering with CNNs
- FlowNet: Learning Optical Flow with Convolutional Networks
- DeepStereo: Learning to Predict New Views from the World’s Imagery
- Deep convolutional filter banks for texture recognition and segmentation
- FaceNet: A Unified Embedding for Face Recognition and Clustering (github.com/cmusatyalab/openface)
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
- 3D ConvNets with Optical Flow Based Regularization
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Rotation-invariant convolutional neural networks for galaxy morphology prediction
- Deep Fried Convnets
- Fractional Max-Pooling
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Learning FRAME Models Using CNN Filters for Knowledge Visualization (code)
- Invariant backpropagation: how to train a transformation-invariant neural network
- Recommending music on Spotify with deep learning
- Conv Nets: A Modular Perspective
- Learning 3D Shape (1) (github.com/danfischetti/shape-classifier)

## Recurrent Neural Networks

- Unitary Evolution Recurrent Neural Networks
- Regularizing RNNs by Stabilizing Activations
- Training recurrent networks online without backtracking
- Modeling sequential data using higher-order relational features and predictive training(github.com/memisevic/grammar-cells)
- Recurrent Neural Network Regularization
- How to Construct Deep Recurrent Neural Networks
- DAG-Recurrent Neural Networks For Scene Labeling
- Long Short-Term Memory (ftp://ftp.idsia.ch/pub/juergen/lstm.pdf)
- Learning Longer Memory in Recurrent Neural Networks
- A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
- A Clockwork RNN
- DRAW: A Recurrent Neural Network For Image Generation
- Gated Feedback Recurrent Neural Networks
- A Recurrent Latent Variable Model for Sequential Data
- ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
- Translating Videos to Natural Language Using Deep Recurrent Neural Networks
- Unsupervised Learning of Video Representations using LSTMs
- Visualizing and Understanding Recurrent Networks
- Advances in Optimizing Recurrent Networks
- Learning Stochastic Recurrent Networks
- Understanding LSTM Networks
- Optimizing RNN performance

## Convolutional Recurrent Neural Networks

- Recurrent Spatial Transformer Networks (github.com/skaae/recurrent-spatial-transformer-code)
- Recurrent Models of Visual Attention
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Describing Multimedia Content using Attention-based Encoder–Decoder Networks

## Adversarial Neural Networks

- Improving Back-Propagation by Adding an Adversarial Gradient
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
- Adversarial Autoencoders

## Autoencoders

- Correlational Neural Networks
- Optimizing Neural Networks that Generate Images (github.com/mrkulk/Unsupervised-Capsule-Network)
- Auto-Encoding Variational Bayes
- Analyzing noise in autoencoders and deep networks
- MADE: Masked Autoencoder for Distribution Estimation (github.com/mgermain/MADE)
- Winner-Take-All Autoencoders (github.com/stephenbalaban/convnet)
- k-Sparse Autoencoders (github.com/stephenbalaban/convnet)
- Zero-bias autoencoders and the benefits of co-adapting features
- Importance Weighted Autoencoders (github.com/yburda/iwae)
- Generalized Denoising Auto-Encoders as Generative Models
- Marginalized Denoising Auto-encoders for Nonlinear Representations
- Real-time Hebbian Learning from Autoencoder Features for Control Tasks
- Procedural Modeling Using Autoencoder Networks (pdf) (youtu.be/wl3h4S1g2u4)
- Is Joint Training Better for Deep Auto-Encoders?
- Towards universal neural nets: Gibbs machines and ACE
- Transforming Auto-encoders
- Discovering Hidden Factors of Variation in Deep Networks

## Restricted Boltzmann Machines

- The wake-sleep algorithm for unsupervised neural networks
- An Infinite Restricted Boltzmann Machine
- Quantum Inspired Training for Boltzmann Machines
- Training Bidirectional Helmholtz Machines

## Biologically Plausible Learning

- How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation
- How Important is Weight Symmetry in Backpropagation?
- Random feedback weights support learning in deep neural networks

## Supervised Learning

- Fast Label Embeddings via Randomized Linear Algebra
- Locally Non-linear Embeddings for Extreme Multi-label Learning
- Efficient and Parsimonious Agnostic Active Learning

## Unsupervised Learning

- Towards Principled Unsupervised Learning
- Index-learning of unsupervised low dimensional embedding
- An Analysis of Unsupervised Pre-training in Light of Recent Advances (github.com/ifp-uiuc/an-analysis-of-unsupervised-pre-training-iclr-2015)
- Is Joint Training Better for Deep Auto-Encoders?
- Unsupervised Feature Learning from Temporal Data
- Learning to Linearize Under Uncertainty
- Semi-Supervised Learning with Ladder Network (github.com/arasmus/ladder)
- Semi-Supervised Learning with Deep Generative Models
- Rectified Factor Networks
- An Analysis of Single-Layer Networks in Unsupervised Feature Learning
- Deep Unsupervised Learning using Nonequilibrium Thermodynamics
- On-the-Fly Learning in a Perpetual Learning Machine

## Reinforcement Learning

- Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
- Prioritized Experience Replay
- Human-level control through deep reinforcement learning (sites.google.com/a/deepmind.com/dqn)
- Playing Atari with Deep Reinforcement Learning
- Universal Value Function Approximators
- Giraffe: Using Deep Reinforcement Learning to Play Chess (bitbucket.org/waterreaction/giraffe)

## Theory

- Deep Kernel Learning
- The Human Kernel
- Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
- Deep Manifold Traversal: Changing Labels with Convolutional Features
- On the Expressive Power of Deep Learning: A Tensor Analysis
- ℓ1-regularized Neural Networks are Improperly Learnable in Polynomial Time
- Provable approximation properties for deep neural networks
- Steps Toward Deep Kernel Methods from Infinite Neural Networks
- On the Number of Linear Regions of Deep Neural Networks
- On the saddle point problem for non-convex optimization
- The Loss Surfaces of Multilayer Networks
- Statistical mechanics of complex neural systems and high dimensional data
- Qualitatively characterizing neural network optimization problems
- An average-case depth hierarchy theorem for Boolean circuits
- An exact mapping between the Variational Renormalization Group and Deep Learning
- Why does Deep Learning work? – A perspective from Group Theory
- A Group Theoretic Perspective on Unsupervised Deep Learning
- Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
- On the Stability of Deep Networks
- Over-Sampling in a Deep Neural Network
- A theoretical argument for complex-valued convolutional networks
- Spectral Representations for Convolutional Neural Networks
- A Probabilistic Theory of Deep Learning
- Deep Convolutional Networks on Graph-Structured Data (github.com/mbhenaff/spectral-lib)
- Learning with Group Invariant Features: A Kernel Perspective
- Randomized algorithms for matrices and data
- Calculus on Computational Graphs: Backpropagation
- Understanding Convolutions
- Groups & Group Convolutions
- Neural Networks, Manifolds, and Topology
- Neural Networks, Types, and Functional Programming
- Causal Entropic Forces
- On the Computability of AIXI
- Physics, Topology, Logic and Computation: A Rosetta Stone

## Quantum Computing

- Analyzing Big Data with Dynamic Quantum Clustering
- Quantum algorithms for supervised and unsupervised machine learning
- Entanglement-Based Machine Learning on a Quantum Computer
- A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification
- Application of Quantum Annealing to Training of Deep Neural Networks
- Quantum Deep Learning
- Experimental Realization of Quantum Artificial Intelligence

## Training Innovations

- Adding Gradient Noise Improves Learning for Very Deep Networks
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- Net2Net: Accelerating Learning via Knowledge Transfer
- Learning the Architecture of Deep Neural Networks
- GradNets: Dynamic Interpolation Between Neural Architectures
- Reducing the Training Time of Neural Networks by Partitioning
- The Effects of Hyperparameters on SGD Training of Neural Networks
- Gradient-based Hyperparameter Optimization through Reversible Learning (github.com/HIPS/hypergrad)
- Learning Ordered Representations with Nested Dropout
- Learning Compact Convolutional Neural Networks with Nested Dropout
- Reducing Overfitting in Deep Networks by Decorrelating Representations
- Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
- Efficient Per-Example Gradient Computations
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Highway Networks
- Random Walk Initialization for Training Very Deep Feedforward Networks
- Deeply-Supervised Nets
- Improving neural networks by preventing co-adaptation of feature detectors
- Maxout Networks
- Regularization of Neural Networks using DropConnect
- Distilling the Knowledge in a Neural Network
- Domain-Adversarial Neural Networks
- Weight Uncertainty in Neural Networks
- Notes on Noise Contrastive Estimation and Negative Sampling
- Scale-invariant learning and convolutional networks
- Empirical Evaluation of Rectified Activations in Convolutional Network
- Deep Boosting (github.com/google/deepboost)
- No Regret Bound for Extreme Bandits

## Parallel Training

- Scalable Distributed DNN Training Using Commodity GPU Cloud Computing
- AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
- Large Scale Distributed Deep Networks
- HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

## Weight Compression

- Tensorizing Neural Networks (github.com/Bihaqo/TensorNet) (github.com/vadimkantorov/tensornet.torch)
- Structured Transforms for Small-Footprint Deep Learning
- An exploration of parameter redundancy in deep networks with circulant projections
- A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding
- Learning both Weights and Connections for Efficient Neural Networks
- Compressing Neural Networks with the Hashing Trick
- Flattened Convolutional Neural Networks for Feedforward Acceleration (github.com/jhjin/flattened-cnn)
- Predicting Parameters in Deep Learning

## Numerical Precision

- Neural Networks with Few Multiplications
- Deep Learning with Limited Numerical Precision
- Low precision storage for deep learning
- 1-Bit Stochastic Gradient Descent and Application to Data-Parallel Distributed Training of Speech DNNs

## Numerical Optimization

- Recursive Decomposition for Nonconvex Optimization (github.com/afriesen/rdis)
- Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
- Adapting Resilient Propagation for Deep Learning
- Accelerating Stochastic Gradient Descent via Online Learning to Sample
- Preconditioned Spectral Descent for Deep Learning
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
- Beyond Convexity: Stochastic Quasi-Convex Optimization
- Graphical Newton
- Gradient Estimation Using Stochastic Computation Graphs
- Equilibrated adaptive learning rates for non-convex optimization
- Path-SGD: Path-Normalized Optimization in Deep Neural Networks
- Deep learning via Hessian-free optimization
- On the importance of initialization and momentum in deep learning
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- ADADELTA: An Adaptive Learning Rate Method
- ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient
- Adam: A Method for Stochastic Optimization
- A sufficient and necessary condition for global optimization
- Unit Tests for Stochastic Optimization
- A* Sampling
- Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
- When Are Nonconvex Problems Not Scary?
- Automatic differentiation in machine learning: a survey

## Motion Planning

- Interactive Control of Diverse Complex Characters with Neural Networks (video)
- Continuous control with deep reinforcement learning
- Continuous Character Control with Low-Dimensional Embeddings
- Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours (youtu.be/oSqHc0nLkm8)
- End-to-End Training of Deep Visuomotor Policies (youtu.be/Q4bMcUk6pcw)
- Deep Spatial Autoencoders for Visuomotor Learning(youtu.be/TsPpoxKST2A)
- From Pixels to Torques: Policy Learning with Deep Dynamical Models (thesis) (github.com/iassael/torch-ddcnn)
- Sampling-based Algorithms for Optimal Motion Planning (youtu.be/r34XWEZ41HA)
- Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic (youtu.be/nsl-5MZfwu4)
- Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs (youtu.be/TQIoCC48gp4) (github.com/utiasASRL/batch-informed-trees)

- Planning biped locomotion using motion capture data and probabilistic roadmaps (youtu.be/cKrcjrdnD-M)
- Stability of Surface Contacts for Humanoid Robots: Closed-Form Formulae of the Contact Wrench Cone for Rectangular Support Areas (github.com/Tastalian/surface-contacts-icra-2015)

## Simulation

## Hardware

- Towards Trainable Media: Using Waves for Neural Network-Style Training
- Random Projections through multiple optical scattering: Approximating kernels at the speed of light
- VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
- Training and operation of an integrated neuromorphic network based on metal-oxide memristors
- AHaH Computing–From Metastable Switches to Attractors to Machine Learning
- Finding a roadmap to achieve large neuromorphic hardware systems

## Cognitive Architectures

- A Large-Scale Model of the Functioning Brain
- Derivation of a novel efficient supervised learning algorithm from cortical-subcortical loops
- A Minimal Architecture for General Cognition (github.com/mikegashler/manic)

## Computational Creativity

- Inceptionism: Going Deeper into Neural Networks
- A Neural Algorithm of Artistic Style
- The Unreasonable Effectiveness of Recurrent Neural Networks (github.com/karpathy/char-rnn)
- GRUV: Algorithmic Music Generation using Recurrent Neural Networks (github.com/MattVitelli/GRUV)
- Composing Music With Recurrent Neural Networks (github.com/hexahedria/biaxial-rnn-music-composition)

## Cryptography

## Distributed Computing

## Clustering

- Convolutional Clustering for Unsupervised Learning
- Deep clustering: Discriminative embeddings for segmentation and separation
- Clustering is Easy When ….What?
- Clustering by fast search and find of density peaks