Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
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Updated
Mar 24, 2023 - Python
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"
Tripod is a tool/ML model for computing latent representations for large sequences
ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"
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
Anime Style Illustration Specific Image Search App with ViT Tagger x LSI
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.
Working towards deliverable 5.3
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".
📜 [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
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