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
TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees
Alireza Khataei, Kia Bazargan
FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin, Yineng Zhang, Stephanie Wang, Tianqi Chen, Baris Kasikci, Vinod Grover, Arvind Krishnamurthy, Luis Ceze
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference
Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi, Takamitsu Matsubara
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
En Fu, Yanyan Hu
A Survey on LLM Inference-Time Self-Improvement
Xiangjue Dong, Maria Teleki, James Caverlee
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Griffin Adams, Jeremy Howard, Iacopo Poli
Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
Eitan Wagner, Nitay Alon, Joseph M. Barnby, Omri Abend
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
Sebastian Salazar, Michal Kucer, Yixin Wang, Emily Casleton, David Blei
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference
Siyuan Wang, Dianyi Wang, Chengxing Zhou, Zejun Li, Zhihao Fan, Xuanjing Huang, Zhongyu Wei