Search Results for author:
Found 8 papers, 2 papers with code
Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning
First, we compare performance of SI to Bayesian reinforcement learning (RL) schemes designed to solve similar problems.
Barriers and Solutions to the Adoption of Clinical Tools for Computational Psychiatry
Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena.
Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks
Robust classification is essential in tasks like autonomous vehicle sign recognition, where the downsides of misclassification can be grave.
AdaWCT: Adaptive Whitening and Coloring Style Injection
We show, through experiments on the StarGANv2 architecture, that this generalization, albeit conceptually simple, results in significant improvements in the quality of the generated images.
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.
Reward Maximisation through Discrete Active Inference
Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based 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.
