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Found 6 papers, 3 papers with code
Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation
Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space.
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
This manifold lens provides both clarity as to why some DGMs (e. g. diffusion models and some generative adversarial networks) empirically surpass others (e. g. likelihood-based models such as variational autoencoders, normalizing flows, or energy-based models) at sample generation, and guidance for devising more performant DGMs.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Comparing to 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models, we find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID.
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation
The student is optimized to predict the output of the teacher after multiple decoding steps while the teacher follows the student via a slow-moving average.
Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence
We prove that, initialized with a Gaussian random vector that has sufficiently small variance, iterating the LMC algorithm for $\widetilde{\mathcal{O}}(\lambda^2 d\epsilon^{-1})$ steps is sufficient to reach $\epsilon$-neighborhood of the target in both Chi-squared and Renyi divergence, where $\lambda$ is the logarithmic Sobolev constant of $\nu_*$.
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness
This convergence rate, in terms of $\epsilon$ dependency, is not directly influenced by the tail growth rate $\alpha$ of the potential function as long as its growth is at least linear, and it only relies on the order of smoothness $\beta$.
