Knowledge Barrier
Knowledge barriers in various fields, from AI model training to medical image segmentation, hinder progress by limiting access to information or resources. Current research focuses on mitigating these barriers through techniques like multi-objective learning, improved data representation (e.g., using graph neural networks or knowledge graphs), and innovative algorithms for model compression and robustness (e.g., randomized smoothing in reinforcement learning). Overcoming these barriers is crucial for advancing scientific understanding and enabling broader access to powerful technologies, fostering inclusivity and accelerating innovation across diverse domains.
Papers
Breaking the Reclustering Barrier in Centroid-based Deep Clustering
Lukas Miklautz, Timo Klein, Kevin Sidak, Collin Leiber, Thomas Lang, Andrii Shkabrii, Sebastian Tschiatschek, Claudia Plant
Barriers to Welfare Maximization with No-Regret Learning
Ioannis Anagnostides, Alkis Kalavasis, Tuomas Sandholm
From Barriers to Tactics: A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching
Eric Yang, Tomas Garcia, Hannah Williams, Bhawesh Kumar, Martin Ramé, Eileen Rivera, Yiran Ma, Jonathan Amar, Caricia Catalani, Yugang Jia
DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model
Zhaoyang Mul, Aoming Liang, Mingming Ge, Dashuai Chen, Dixia Fan, Minyi Xu