- Decoupled Neural Interfaces using Synthetic Gradients.
- Explaining Deep Convolutional Neural Networks on Music Classification.
- Accelerating Eulerian Fluid Simulation With Convolutional Networks.
Classic references on Deep Reinforcement Learning:
- Self-Modification of Policy and Utility Function in Rational Agents (Everitt, et. al, 2016).
- WIKIREADING: A Novel Large-scale Language Understanding Task over Wikipedia (Hewlett, et. al, 2016).
- Evaluation of General-Purpose Artificial Intelligence: Why, What & How, (Bieger, et. al, 2016).
- Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
- Instance Normalization: The Missing Ingredient for Fast Stylization (Ulyanov, et. al, 2016).
- Learning Semantic Deformation Flows with 3D Convolutional Networks .
- Costs of extinction risk mitigation. A Cost-Benefit Analysis of the extinction risk mitigation, claiming that the annual cost of reducing the probability of human extinction by 0.01 ...
About a year ago, Google published a seminal paper named ImageNet Classification with Deep Convolutional Neural Networks, together with a blog post, which became known as Inceptionism. This work unveiled not only a new way of composing hallucinating artistic pictures but astonishing new insights on how convolutional neural networks work ...more ...
This is a selection of quintessential papers for anyone starting on Deep Learning (Thanks to Joe Zimmerman):
- ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky, et al., 2014). AlexNet.
- Very Deep Convolutional Networks for large-scale image recognition (Simonyan, et al., 2014). Image classification.
- Improving neural networks by preventing co-adaptation ...
- The Science of Talking with Computers
- Megan Smith: Perspectives on artificial intelligence from the White House.
- NVIDIA Deep Learning Course: Class #1 – Introduction to Deep Learning.
Mastering the Game of Go with Deep Neural Networks and Tree Search. "All games of perfect information have an optimal value function which determines the outcome of the game". , Basically:
- Values networks to evaluate board positions and policy networks to select moves.
- Trained with supervised learning from human expert ...
Machine learning involves tasks that include data sourcing, data ingestion, data transformation, pre-processing data for use in training, training a model, and hosting the model. Additionally, to get value out of machine learning models, we need an architecture and process in place to repeatedly and consistently train new models and ...more ...
The app I wrote was a Python software running in AWS Lambda, which would be triggered by messages from ...more ...