Search Results for author:
Found 11 papers, 5 papers with code
Dialogue Response Generation via Contrastive Latent Representation Learning
In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure.
GrounDial: Human-norm Grounded Safe Dialog Response Generation
Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content.
Bridging Maximum Likelihood and Adversarial Learning via $α$-Divergence
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary.
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
In this paper, we propose a novel Contrastive Log-ratio Upper Bound (CLUB) of mutual information.
APo-VAE: Text Generation in Hyperbolic Space
In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations.
On Fenchel Mini-Max Learning
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling.
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
Adaptation Across Extreme Variations using Unlabeled Domain Bridges
We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation.
Adversarial Text Generation via Feature-Mover's Distance
However, the discrete nature of text hinders the application of GAN to text-generation tasks.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence.
