Summary of Metallm: a High-performant and Cost-efficient Dynamic Framework For Wrapping Llms, by Quang H. Nguyen et al.
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMsby Quang H. Nguyen, Duy C.…
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMsby Quang H. Nguyen, Duy C.…
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equationsby Wei Zhou, Y.F.…
Evaluating Model Bias Requires Characterizing its Mistakesby Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Taylan Cemgil,…
Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learningby Ziyan Wang, Yaxuan…
Physics-Informed Machine Learning for Smart Additive Manufacturingby Rahul Sharma, Maziar Raissi, Y.B. GuoFirst submitted to…
An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Featuresby William Basener,…
Proper losses regret at least 1/2-orderby Han Bao, Asuka TakatsuFirst submitted to arxiv on: 15…
Deflated Dynamics Value Iterationby Jongmin Lee, Amin Rakhsha, Ernest K. Ryu, Amir-massoud FarahmandFirst submitted to…
Learning to Unlearn for Robust Machine Unlearningby Mark He Huang, Lin Geng Foo, Jun LiuFirst…
Unexpected Benefits of Self-Modeling in Neural Systemsby Vickram N. Premakumar, Michael Vaiana, Florin Pop, Judd…