Search Results for author:
Found 4 papers, 0 papers with code
Overparameterization Improves StyleGAN Inversion
We show that this allows us to obtain near-perfect image reconstruction without the need for encoders nor for altering the latent space after training.
Language Models in the Loop: Incorporating Prompting into Weak Supervision
Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy.
The relationship between dynamic programming and active inference: the discrete, finite-horizon case
In this paper, we consider the relation between active inference and dynamic programming under the Bellman equation, which underlies many approaches to reinforcement learning and control.
In-Session Personalization for Talent Search
Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model.
