- GitHub Copilot: Your AI pair programmer – what is all the fuss about? - Jul 5, 2021.
GitHub just released Copilot, a code completion tool on steroids dubbed your "AI pair programmer." Read more about it, and see what all the fuss is about.
AI, Generative Models, GitHub, NLP, Programming
- Adversarial generation of extreme samples - Feb 2, 2021.
In order to mitigate risks when modelling extreme events, it is vital to be able to generate a wide range of extreme, and realistic, scenarios. Researchers from the National University of Singapore and IIT Bombay have developed an approach to do just that.
AI, GANs, Generative Adversarial Network, Generative Models, Sampling
- Deep Learning for Virtual Try On Clothes – Challenges and Opportunities - Oct 16, 2020.
Learn about the experiments by MobiDev for transferring 2D clothing items onto the image of a person. As part of their efforts to bring AR and AI technologies into virtual fitting room development, they review the deep learning algorithms and architecture under development and the current state of results.
Computer Vision, Deep Learning, Fashion, Generative Adversarial Network, Generative Models, Humans, Image Generation
- Markov Chains: How to Train Text Generation to Write Like George R. R. Martin - Nov 29, 2019.
Read this article on training Markov chains to generate George R. R. Martin style text.
Generative Models, Markov Chains, NLP, Text Analytics
- When Too Likely Human Means Not Human: Detecting Automatically Generated Text - May 23, 2019.
Passably-human automated text generation is a reality. How do we best go about detecting it? As it turns out, being too predictably human may actually be a reasonably good indicator of not being human at all.
Generative Models, NLP, Text Analytics
- Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
Pages: 1 2
Deep Learning, GANs, Generative Adversarial Network, Generative Models, MNIST, Neural Networks, Python
- More Deep Learning “Magic”: Paintings to photos, horses to zebras, and more amazing image-to-image translation - Apr 17, 2017.
This is an introduction to recent research which presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
Deep Learning, Generative Adversarial Network, Generative Models, Torch
- Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware - Oct 7, 2016.
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
Canada, Courses, Deep Learning, Generative Models, Geoff Hinton, Machine Learning, Reddit, xkcd
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
GANs, Generative Models, Keras, MNIST
- Are Deep Neural Networks Creative? - May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton
- Deep Learning Transcends the Bag of Words - Dec 7, 2015.
Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.
Beer, Deep Learning, Generative Models, Recurrent Neural Networks, Zachary Lipton
