We study the problem of in-context learning (ICL) with large language mo...
This work aims at decreasing the end-to-end generation latency of large
...
Generative Pre-trained Transformer (GPT) models have exhibited exciting
...
Diffusion probabilistic models (DPMs) are a new class of generative mode...
Generating differentially private (DP) synthetic data that closely resem...
Suppose we want to train text prediction models in email clients or word...
Data sharing between different parties has become increasingly common ac...
The privacy implications of generative adversarial networks (GANs) are a...
We study the problem of learning generative adversarial networks (GANs) ...
Generative adversarial networks (GANs) are often billed as "universal
di...
Leveraging machine-learning (ML) techniques for compiler optimizations h...
Spectral normalization (SN) is a widely-used technique for improving the...
Limited data access is a substantial barrier to data-driven networking
r...
Training disentangled representations with generative adversarial networ...
We study the problem of learning conditional generators from noisy label...
Generative adversarial networks (GANs) are innovative techniques for lea...