Standard protocols for benchmarking self-supervised models involve using a linear or k-nearest neighbor classification on frozen features of the learned model. However, both evaluations are sensitive to hyperparameters making the evaluation and comparison complicated. Read more
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
Graph driven machine learning has seen a surge of interest in the past few years with several applications in social sciences, biology, and network analysis, to name a few. However, in some scenarios, no graph is given a priori and one one has to infer and construct a graph to fit the data given. Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
In this post, we will look at a particular view of uncertainty in modern deep learning systems using droput Read more
Generative modeling is a branch of machine learning that attempts at modeling the probability distribution of high dimensional data, for example - images Read more
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
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