Deep-Learning Model Exploration and Development for NLP

[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Adaptive and Focusing Neural Layers for Multi-Speaker Separation Problem

Graph SuperResolution Network using geometric deep learning.

Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)

Deep learning architectures for in-air hand gesture recognition

Deep Learning architectures in Tensorflow Keras, and PyTorch.

Exploring RL ideas for deep neural network hyper-parameter search

Implementing and training/testing popular model architectures on the CIFAR10 dataset.

Deep Learning papers reading roadmap for anyone who is eager to learn this amazing tech!

🌻 Deep Learning models implemented in PyTorch Lightning

Code release for "Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series" (Oh, Wang, Tang, Sjoding, Wiens), MLHC 2019. https://arxiv.org/abs/1906.02898

Code release for "Learning to Exploit Invariances in Clinical Time-Series Data Using Sequence Transformer Networks" (Oh, Wang, Wiens), MLHC 2018. https://arxiv.org/abs/1808.06725

Notes on ML and DL with jupyter notebooks (python)

# Andoka-2 H.5 Andoka now TV

Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords. Hence, tokenization can be broadly classified into 3 types – word, character, and subword (n-gram characters) tokenization.

My experimentations with Keras