Scientific Inference
Scientific inference, the process of drawing conclusions from data, is a core challenge across numerous scientific fields, with current research focusing on improving efficiency and accuracy. This involves developing novel algorithms and architectures, such as those based on Bayesian networks, diffusion transformers, and autoregressive models, to optimize inference processes in various contexts, including large language models and image processing. These advancements are crucial for accelerating scientific discovery and enabling real-world applications in areas like personalized medicine, legal tech, and industrial automation, where efficient and reliable inference is paramount. The emphasis is on addressing computational bottlenecks and improving the reliability of inferences, particularly in scenarios with limited data or complex models.
Papers
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff
Inference of Utilities and Time Preference in Sequential Decision-Making
Haoyang Cao, Zhengqi Wu, Renyuan Xu
Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
Jiantong Jiang, Zeyi Wen, Peiyu Yang, Atif Mansoor, Ajmal Mian
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Vittorio Cannistraci