Some Code I've Written
It all started when I took my first computer science class in highschool, and going through the many trials and tribulations of programming. Since then, I’ve been enjoying coding and here you can find some of the programs I’ve written.
Self-Paced Learning with Adaptive Deep Visual Embeddings (SPL-ADVisE)
Randomized mini-batches might not be an optimal training curriculum for deep networks. Our paper comes up with an insightful and general method for adaptive curriculum creation by extending self-paced learning with diversity. We show state-of-the-art convergence speeds to optimal test performance on MNIST, FashionMNIST, CIFAR-10 and CIFAR-100. From our BMVC 2018 paper.
Magnet Loss for Deep Metric Learning
PyTorch implementation of the Magnet Loss based on the paper Metric Learning with Adaptive Density Discrimination by Oren Rippel, Piotr Dollar, Manohar Paluri, Lubomir Bourdev from Facebook AI Research (FAIR). From ICLR 2016.
VAE with Gumbel-Softmax
TensorFlow implementation of a Variational Autoencoder with Gumbel-Softmax Distribution based on the papers from Google Brain and DeepMind: Categorical Reparametrization with Gumbel-Softmax by Maddison, Mnih and Teh, The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables by Jang, Gu and Poole, and REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models by Tucker, Mnih, Maddison and Sohl-Dickstein.
Provisioning AWS Spot Instances for Deep Learning
A terraform module for provisioning EC2-based Spot Instances on AWS, specifically for Deep Learning workloads on Amazon’s GPU instances, by taking advantage of automation and friendly declarative configurations.
Anaconda Environments for Deep Learning
Several CPU-based and GPU-based anaconda environments for various deep learning frameworks such as PyTorch, TensorFlow and Theano.
- Ackermann Quicksort
A comparative analysis of recursion in C and Python for the Ackermann function and Quicksort algorithm.