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
ACM CHIL 2020: "Survival Cluster Analysis"
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)
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".
Official repository for the "Multiple wavefield solutions in physics-informed neural networks using latent representation" paper.
Code associated with the paper "Prior Image-Constrained Reconstruction using Style-Based Generative Models" accepted to ICML 2021.
A study on the effect of normalization in predictions by CNN models
ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"
📜 [MIDL 2022] "Sensor to Image Heterogeneous Domain Adaptation Network", Ishikaa Lunawat, Vignesh S, S P Sharan
Investigate mapping of articulations from the image space to the latent space using neural networks.
Latent Representation and Exploration of Images Using Variational AutoEncoders
Anime Style Illustration Specific Image Search App with ViT Tagger x LSI
TensorFlow code and LaTex for Bachelor Thesis: Understanding Variational Autoencoders' Latent Representations of Remote Sensing Images 🌍
TensorFlow Deep Feature Consistent VAE Implementation on the Kaggle Fashion Dataset
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.
Graph Representation Analysis for Connected Embeddings
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