Much Progress
Research on "much progress" spans diverse fields, focusing on improving the reliability and interpretability of machine learning models, particularly in robotics, natural language processing, and computer vision. Current efforts concentrate on developing robust evaluation metrics, addressing data imbalances, and refining model architectures like transformers and generative adversarial networks (GANs) to enhance performance and mitigate biases. This work is crucial for advancing the trustworthiness and practical applicability of AI across various domains, from autonomous systems to biomedical applications.
Papers
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks
Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen