A PyTorch reinforcement learning library centered around reproducible and generalizable algorithm implementations.
Neural Correlates for Reinforcement Learning
A review of the connections between neuroscience and reinforcement learning.
An exploratory and experimental research into the applications of modern deep learning breakthroughs in traditional time-series analysis approaches.
Alzheimer's Dementia Recognition from Spontaneous Speech. The aim is unbiased early detection of cognitive decline from multi-modal data.
SNNs to Validate Experimental Results
Building computational spiking neural network models to model neural functioning and thereby validate experimental results.
DAFL(Data free learning) is an unsupervised Knowledge Distillation technique. We are trying to apply IKD on this technique for smaller datasets as of now like MNIST/CIFAR.
FlashFill Implementation in Julia
Classification of body keypoint trajectories of gesture co-occurring with time expressions
Add Gen converter to ArviZ.jl (Julia)
Creating Benchmark Datasets for Object Recognition with Event-based Cameras
Normalizing Flows for Fast Detector Simulation
A Python toolbox for computing high-order information in neuroimaging
Applying AI capabilities to address Operations challenges in ECMWF Products Team
Stock market Modelling using Reinforcement Learning
This project in collaboration with ISI Kolkata aims to apply deep Reinforcement learning to financial trading markets in order to develop a Markov decision process which will suitably capture any general financial market.
Word2Brain2Image: Visual Reconstruction from Spoken Word Representations
Collected EEG data of subjects listening to spoken object words. We attempted to use deep generative models to generate images of objects using only this EEG data, in order to study internal representations in visual association areas (BA 18,19) of the brain.
Research Project in unofficial collaboration with TCS Research. Applying State of the art models for pneumonia detection on RSNA pneumonia detection dataset. Tested InceptionNet-v3, DenseNet121 and explored Mask RCNN applicability for the dataset. Got 83.8% and 77.9% classification accuracy respectively.
Emotion Recognition From EEG Signals
A project with the aim to use deep convolutional networks on EEG signals in order to predict valence and arousal values from them.
Dynamic Gesture Recognition for controlling portable devices
Using detected dynamic gestures such as swiping left/right, closing fist, etc. to control portable devices ( multitasking / controlling video playback, etc. )