Difficulties in training a Generative Adversarial Network
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
Posts by Category
experiments
A notion of uncertainty in modern neural networks
In this post, we will look at a particular view of uncertainty in modern deep learning systems using droput Read more
A preliminary on generative modeling using neural networks
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
Neural network compression using optimal brain damage
Model pruning in neural networks was the answer I ended up with when I got wondering about the workings of dropout, dropconnect and papers like Do Deep Nets Really Need to be Deep? Read more
A brief introduction to activation functions in neural networks
Neural networks can be viewed as layers of building blocks neurons made up of weights, biases and activation function. A fundamental understanding of how these basic units function could help one in achieving one’s objective Read more
research
Efficient graph construction to represent images
Image processing over the years has evolved from simple linear averaging filters to highly adaptive non linear filtering operations such as the bilateral filter (BF), moving least squares, BM3D and LARK to name a few. Read more