Deep Learning architectures implemented in PyTorch Lightning
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Updated
Jun 23, 2024 - Python
Deep Learning architectures implemented in PyTorch Lightning
Deep-Learning Model Exploration and Development for NLP
Deep Learning architectures in Tensorflow Keras, and PyTorch.
Deep Learning papers reading roadmap for anyone who is eager to learn this amazing tech!
DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.
Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)
A general framework for cascade correlation architectures in Python with wrappers to keras, tensorflow and sklearn
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.
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
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
Graph SuperResolution Network using geometric deep learning.
Implementing and training/testing popular model architectures on the CIFAR10 dataset.
[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Notes on ML and DL with jupyter notebooks (python)
Exploring RL ideas for deep neural network hyper-parameter search
Adaptive and Focusing Neural Layers for Multi-Speaker Separation Problem
My experimentations with Keras
Deep learning architectures for in-air hand gesture recognition
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