Search Results for author:
Found 5 papers, 4 papers with code
Automated Classification of Model Errors on ImageNet
To address this, new label-sets and evaluation protocols have been proposed for ImageNet showing that state-of-the-art models already achieve over 95% accuracy and shifting the focus on investigating why the remaining errors persist.
Human-Guided Fair Classification for Natural Language Processing
While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e. g., in cases of asymmetric counterfactuals).
Latent Space Smoothing for Individually Fair Representations
This enables us to learn individually fair representations that map similar individuals close together by using adversarial training to minimize the distance between their representations.
PODS: Policy Optimization via Differentiable Simulation
Current reinforcement learning (RL) methods use simulation models as simple black-box oracles.
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
In this paper we quantify the effects of the parameter $\beta$ on the model performance and disentanglement.
