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
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'.
Tripod is a tool/ML model for computing latent representations for large sequences
Variational Interpretable Concept Embeddings
Code for our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)
Code associated with the paper "Prior Image-Constrained Reconstruction using Style-Based Generative Models" accepted to ICML 2021.
ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"
Investigate mapping of articulations from the image space to the latent space using neural networks.
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
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