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Natural Language Processing
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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 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
Deep Learning
Neural Networks and Deep Learning
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
Model Zoo
word2vec
GoogleNews-vectors-negative300.bin.gz
freebase-vectors-skipgram1000.bin.gz
GloVe
SENNA
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
)
Improving Caffe: Some Refactoring
(
github.com/Yangqing/caffe2
)
Theano: A CPU and GPU Math Compiler in Python
(
github.com/Theano/Theano
)
Theano: new features and speed improvements
Blocks and Fuel: Frameworks for deep learning
(
github.com/mila-udem/blocks
) (
github.com/mila-udem/blocks-examples
) (
github.com/mila-udem/fuel
)
[Announcing Computation Graph Toolkit](
http://joschu.github.io/index.html#Announcing
CGT "John Schulman") (
github.com/joschu/cgt
)
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
)
ND4J: N-Dimensional Arrays for Java N-Dimensional Scientific Computing for Java
(
github.com/deeplearning4j/nd4j
)
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
Reinforcement Learning Neural Turing Machines
Structured Memory for Neural Turing Machines
Memory Networks
(
github.com/facebook/MemNN
)
End-To-End Memory Networks
Learning to Transduce with Unbounded Memory
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
(
github.com/facebook/Stack-RNN
)
Learning Context Free Grammars: Limitations of a Recurrent Neural Network with an External Stack Memory
A connectionist symbol manipulator that discovers the structure of context-free languages
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
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
Distributed Representations of Words and Phrases and their Compositionality
Exploiting Similarities among Languages for Machine Translation
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
Character-level Convolutional Networks for Text Classification
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
)
LSTM: A Search Space Odyssey
Grid Long Short-Term Memory
Depth-Gated LSTM
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
Mastering the Game of Go with Deep Neural Networks and Tree Search
Convolutional Recurrent Neural Networks
Recurrent Spatial Transformer Networks
(
github.com/skaae/recurrent-spatial-transformer-code
)
Recurrent Models of Visual Attention
Multiple Object Recognition with Visual Attention
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Describing Multimedia Content using Attention-based Encoder--Decoder Networks
Adversarial Neural Networks
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
Marginalized Denoising Autoencoders for Domain Adaptation
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
A simple algorithm that discovers efficient perceptual codes
Reweighted Wake-Sleep
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
Difference Target Propagation
Towards Biologically Plausible Deep Learning
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
Fast Label Embeddings for Extremely Large Output Spaces
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
)
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images
Lateral Connections in Denoising Autoencoders Support Supervised Learning
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
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
Identifying and attacking the saddle point problem in high-dimensional 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
Batch Normalized Recurrent Neural Networks
Highway Networks
Training Very Deep Networks
Random Walk Initialization for Training Very Deep Feedforward Networks
Deeply-Supervised Nets
Improving neural networks by preventing co-adaptation of feature detectors
Efficient batchwise dropout training using submatrices
(
github.com/btgraham/Batchwise-Dropout
)
Dropout Training for Support Vector Machines
Dropout as data augmentation
Partitioning Large Scale Deep Belief Networks Using Dropout
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
)
Tensorizing Neural Networks presentation
Tensor-Train Decomposition
(
pdf
) (
github.com/oseledets/TT-Toolbox
)
Spectral tensor-train decomposition
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
)
Recursive Decomposition for Nonconvex Optimization: Supplementary Material
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
Preconditioned Spectral Descent for Deep Learning: Supplemental Material
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
A Level-Value Estimation Method and Stochastic Implementation 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
Data-Driven Fluid Simulations using Regression Forests
(
vimeo.com/144267433
) (
vimeo.com/144266101
)
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
How to Build a Brain: A Neural Architecture for Biological Cognition
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
DeepDream - a code example for visualizing Neural Networks
(
github.com/google/deepdream
)
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
Crypto-Nets: Neural Networks over Encrypted Data
Distributed Computing
Dimension Independent Similarity Computation
Dimension Independent Matrix Square using MapReduce
All-pairs similarity via DIMSUM
A Fast, Minimal Memory, Consistent Hash Algorithm
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
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