deep learning

Generative Multimodal Learning for Reconstructing Missing Modality

Training a latent variable based variational inference model on multimodal data in order to perform inference with all possible combinations of missing modalities.

Generative Adversarial Networks: Reproducibility Study

A reproducibility test, ablation studies and extension of the seminal Generative Adversarial Networks paper

Modified MNIST [Kaggle]

Identifying the highest number present in modified MNIST images containing multiple handwritten digits on random backgrounds using deep learning

Reddit Comment Classification [Kaggle]

We analyze different Machine Learning models to process Reddit data and develop a supervised classification model that can predict what community a certain comment came from.

Generic Extraction Module (G.E.M)

Trained a biLSTM model using both word and character level embeddings for information retrieval from text OCR outputs of ID cards

Dory OCR

We created a state of the art Optical Character Recognition Engine specifically for Indian ID cards using a pipeline for document layout detection, foreground extraction, text detection, recognition and postprocessing

Cropnet

Regression based deep learning models for automatically cropping document as foreground extraction(segmentation) task

March Madness [Kaggle]

Applying Machine Learning to March Madness College Basketball tournament for predicting tournament match results

Activity Recognition

Using traditional computer vision with deep learning algorithms for Anomalous activity detection from CCTV camera feed

Multilingual Speech Recognition

Hindi and English digit recognition using MFCC features and five different neural networks and their performance evaluation under different conditions