Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch

Graph Representation Analysis for Connected Embeddings

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.

Code for our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)

ACM CHIL 2020: "Survival Cluster Analysis"

ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"

Tripod is a tool/ML model for computing latent representations for large sequences

Variational Interpretable Concept Embeddings

Code associated with the paper "Prior Image-Constrained Reconstruction using Style-Based Generative Models" accepted to ICML 2021.

Simple Pytorch Implementation of BYOL: Bootstrap Your Own Latent(https://arxiv.org/abs/2006.07733) [Colab Version Available]

A study on the effect of normalization in predictions by CNN models

Official repository for the "Multiple wavefield solutions in physics-informed neural networks using latent representation" paper.

Investigate mapping of articulations from the image space to the latent space using neural networks.

TensorFlow code and LaTex for Bachelor Thesis: Understanding Variational Autoencoders' Latent Representations of Remote Sensing Images 🌍

Latent-Explorer is the Python implementation of the framework proposed in the paper "Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph".

Working towards deliverable 5.3

📜 [MIDL 2022] "Sensor to Image Heterogeneous Domain Adaptation Network", Ishikaa Lunawat, Vignesh S, S P Sharan

Latent Representation and Exploration of Images Using Variational AutoEncoders

This algorithm exploits the relationships between variables to improve the reconstruction performance of the variational autoencoder (VAE). A correlation score was used as the metric to group the features via a distance-based clustering method. The resulting clusters served as inputs for the Attention-Based VAE.