Investigate mapping of articulations from the image space to the latent space using neural networks.
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Updated
Nov 3, 2022 - Python
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
ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"
Code associated with the paper "Prior Image-Constrained Reconstruction using Style-Based Generative Models" accepted to ICML 2021.
Variational Interpretable Concept Embeddings
Code for our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)
Tripod is a tool/ML model for computing latent representations for large sequences
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'.
Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
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