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Found 12 papers, 5 papers with code
CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly.
Estimation and Inference in Distributional Reinforcement Learning
This implies the distributional policy evaluation problem can be solved with sample efficiency.
Distributional Reinforcement Learning
reinforcement-learning
Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach
Additionally, we propose the Debiased LPSA (DLPSA) as a practical application of our jump diffusion approximation result.
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data
We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data.
Layout-Bridging Text-to-Image Synthesis
Specifically, we formulate the text-to-layout generation as a sequence-to-sequence modeling task, and build our model upon Transformer to learn the spatial relationships between objects by modeling the sequential dependencies between them.
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings.
Statistical Estimation and Inference via Local SGD in Federated Learning
Both the methods are communication efficient and applicable to online data.
Intervention Generative Adversarial Nets
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
Intervention Generative Adversarial Networks
In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem.
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly.
Lipschitz Generative Adversarial Nets
By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem.
Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs.
