Add to ORKGThe thesis deals with the design of a deep learning model that can learn a generative process realizing unconstrained tree transductions. The model is based on an extension of the popular Variational Autoencoder framework to allow conditioning the generative process on tree structured inputs and to generate tree-structured predictions. It has been realized an efficient Tensorlow implementation of the proposed model, which has been validated on Arithmetic Expression trees and Neural Machine Translatio
Advances in variational inference enable parameterisation of probabilistic models by deep neural net...
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for tas...
International audienceConditional Generative Models are now acknowledged an essential tool in Machin...
Deep generative models have been wildly successful at learning coherent latent representations for c...
The thesis describes a deep neural network for learning tree-to-tree transductions. The proposed app...
We propose a method for learning the dependency structure between latent variables in deep latent va...
In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-ar...
In this paper we propose a conditional generative modelling (CGM) approach for unsupervised disentan...
Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An...
Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An...
We discuss an autoencoder model in which the encoding and decoding functions are im-plemented by dec...
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic en...
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic en...
We discuss an autoencoder model in which the encoding and decoding functions are implemented by deci...
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for tas...
Advances in variational inference enable parameterisation of probabilistic models by deep neural net...
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for tas...
International audienceConditional Generative Models are now acknowledged an essential tool in Machin...
Deep generative models have been wildly successful at learning coherent latent representations for c...
The thesis describes a deep neural network for learning tree-to-tree transductions. The proposed app...
We propose a method for learning the dependency structure between latent variables in deep latent va...
In natural language processing (NLP), Transformer is widely used and has reached the state-of-the-ar...
In this paper we propose a conditional generative modelling (CGM) approach for unsupervised disentan...
Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An...
Both models are based on the encoder-decoder neural network structure to a learn latent space. A) An...
We discuss an autoencoder model in which the encoding and decoding functions are im-plemented by dec...
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic en...
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic en...
We discuss an autoencoder model in which the encoding and decoding functions are implemented by deci...
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for tas...
Advances in variational inference enable parameterisation of probabilistic models by deep neural net...
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for tas...
International audienceConditional Generative Models are now acknowledged an essential tool in Machin...